Diagnostic mirna markers for alzheimer

ABSTRACT

The invention relates to methods for diagnosing Alzheimer&#39;s Disease (AD) with miRNA markers. Diagnosis of AD Towards the identification of biomarkers for diagnosis of AD, a comprehensive analysis of miRNA expression patterns was obtained. Significantly deregulated miRNAs were identified.

PRIORITY STATEMENT

This application is a national phase application under 35 U.S.C. §371 ofPCT International Application No. PCT/EP2013/072567 which has anInternational filing date of 29 Oct. 2013, which designated the UnitedStates of America, and which claims priority to European patentapplication number 12192974.9 filed 16 Nov. 2012. The entire contents ofeach patent application referenced above are hereby incorporated byreference.

REFERENCE TO A SEQUENCE LISTING

This application contains references to amino acid sequences and/ornucleic acid sequences which have been submitted concurrently herewithas the sequence listing text file 61486342_1.TXT file size 33.9KiloBytes (KB), created on 27 Apr. 2015. The aforementioned sequencelisting is hereby incorporated by reference in its entirety pursuant to37 C.F.R. §1.52(e)(5).

FIELD OF THE INVENTION

The invention relates to novel markers for diagnosing Alzheimer'sdisease.

BACKGROUND OF THE INVENTION

Very recently, molecular diagnostics has increasingly gained inimportance. It has found an entry into the clinical diagnosis ofdiseases (inter alia detection of infectious pathogens, detection ofmutations of the genome, detection of diseased cells and identificationof risk factors for predisposition to a disease).

In particular, through the determination of gene expression in tissues,nucleic acid analysis opens up very promising new possibilities in thestudy and diagnosis of disease.

Nucleic acids of interest to be detected include genomic DNA, expressedmRNA and other RNAs such as MicroRNAs (abbreviated miRNAs). MiRNAs are anew class of small RNAs with various biological functions (A. Keller etal., Nat Methods. 2011 8(10):841-3). They are short (average of 20-24nucleotide) ribonucleic acid (RNA) molecules found in eukaryotic cells.Several hundred different species of microRNAs (i.e. several hundreddifferent sequences) have been identified in mammals. They are importantfor post-transcriptional gene-regulation and bind to complementarysequences on target messenger RNA transcripts (mRNAs), which can lead totranslational repression or target degradation and gene silencing. Assuch they can also be used as biologic markers for research, diagnosisand therapy purposes.

Alzheimer's disease (AD), also known in medical literature as Alzheimerdisease, is the most common form of dementia. Alzheimer's disease ischaracterised by loss of neurons and synapses in the cerebral cortex andcertain subcortical regions and leads to a gross degeneration in theseregions. In AD protein misfolding and aggregation (formation ofso-called “plaques”) in the brain is caused by accumulation ofabnormally folded A-beta and tau proteins in the affected tissues.

Early symptoms are often mistaken to be age-related problems. In theearly stages, the most common symptom is difficulty in rememberingrecent events. When AD is suspected, the diagnosis is usually confirmedwith functional tests that evaluate behaviour and cognitive abilities,often followed by imaging analysis of the brain. Imaging methods usedfor this purpose include computed tomography (CT), magnetic resonanceimaging (MRI), single photon emission computed tomography (SPECT), andpositron emission tomography (PET). In a patients already havingdementia, SPECT appears to be superior in differentiating Alzheimer'sdisease from other possible causes, compared with the usual attemptsemploying mental testing and medical history analysis. A new techniqueknown as PiB PET has been developed for directly and clearly imagingbeta-amyloid deposits in vivo using a tracer that binds selectively tothe beta-amyloid deposits. Beta-amyloid deposits. Recently, a miRNAdiagnostic test from serum has been proposed (Geekiyanage et al., ExpNeurol. 2012 June; 235(2):491-6.)

Symptoms can be similar to other neurological disorders. Diagnosis canbe time consuming, expensive and difficult. In particular, the reliableand early diagnosis of Alzheimer based on non-invasive molecularbiomarkers remains a challenge. Till today, early diagnosis of ADremains a great challenge. So far, findings of an autopsy or biopsyrepresent the most reliable diagnostics for this common disease

The attempt to report the presence of beta-amyloid not only in thebrain, but also in other tissues, e.g. the skin, showed only limitedrelevance for diagnosing AD. (Malaplate-Armand C, Desbene C, Pillot T,Olivier J L. Diagnostic biologique de la maladie d'Alzheimer: avancées,limites et perspectives. Rev Neurol 2009; 165:511-520). Thus, in therecent past, different imaging as well as in vitro diagnostic markershave been proposed in order to improve the AD diagnosis. Mostimportantly, biomarkers that can detect AD in pre-clinical stages are inthe focus, however, such markers can so far be only reliably detected incerebrospinal fluid (CSF). One prominent example is the combination ofbeta-amyloid-1-42 and tau. In addition, molecular genetics analyses ofsingle nucleotide polymorphisms (SNPs) in the DNA of patients have beenproposed to provide a risk estimation of the presence of AD. In additionto variants in genes, several studies have described an associationbetween AD and genetic variation of mitochondrial DNA (mtDNA). Here, noconsistent evidence for the relation of mtDNA variants and AD could bereported Hudson G, Sims R, Harold D, et al.; GERAD1 Consortium. Noconsistent evidence for association between mtDNA variants and Alzheimerdisease. Neurology 2012; 78:1038-1042. However, although theheritability of AD is comparably high (60-80%), epigenetic andpersistent factors also may play an important role.

Therefore, there exists an unmet need for an efficient, simple, reliablediagnostic test for AD.

OBJECT OF THE INVENTION

The technical problem underlying the present invention is to providebiological markers allowing to diagnose, screen for or monitorAlzheimer's disease, predict the risk of developing Alzheimer's disease,or predict an outcome of Alzheimer's disease.

SUMMARY OF THE INVENTION

Before the invention is described in detail, it is to be understood thatthis invention is not limited to the particular component parts of theprocess steps of the methods described as such methods may vary. It isalso to be understood that the terminology used herein is for purposesof describing particular embodiments only, and is not intended to belimiting. It must be noted that, as used in the specification and theappended claims, the singular forms “a,” “an” and “the” include singularand/or plural referents unless the context clearly dictates otherwise.It is also to be understood that plural forms include singular and/orplural referents unless the context clearly dictates otherwise. It ismoreover to be understood that, in case parameter ranges are given whichare delimited by numeric values, the ranges are deemed to include theselimitation values.

In its most general terms, the invention relates to a collection ofmiRNA markers useful for the diagnosis, prognosis and prediction ofAlzheimer's Disease.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows the distribution of reads obtained by high throughputsequencing. The left pie chart shows that 95% of known miRNAs belong toone miRNA while all other 1.000 detected known markers make up only 5%.The novel detected miRNAs on the right hand side are much less abundantthan the most frequently detected miRNA.

FIG. 2 shows the ROC curve for the most up-regulated miRNA, geneexpression data obtained by NGS. X-axis: specificity, y-axis:sensitivity.

FIG. 3 shows the ROC curve for the most down-regulated miRNA, geneexpression data obtained by NGS. X-axis: specificity, y-axis:sensitivity.

FIG. 4 shows increased performance by using marker combinations. Thex-axis shows the number of miRNAs, the y axis shows the classificationof performance, gene expression data obtained by NGS (solid line:sensitivity, broken line: accuracy, broken and dotted line:specificity).

FIG. 5 shows a combined score of AD, MCI and controls for the 7-markersignature brain-mir-112, hsa-miR-5010-3p, hsa-miR-103a-3p, hsa-miR-107,hsa-let-7d-3p, hsa-miR-532-5p, and brain-mir-161. The combined score(y-axis) was obtained using high throughput sequencing.

FIG. 6 shows the ROC curve for the 7-marker signature of FIG. 5, geneexpression data obtained by NGS.

FIG. 7 shows the qRT-PCR validation of selected miRNAs, the up-regulatedmiRNAs brain-mir-112, brain-mir-161, hsa-let-7d-3p, hsa-miR-5010-3p,hsa-miR-26b-3p, hsa-miR-26a-5p, hsa-miR-1285-5p, and hsa-miR-151a-3p aswell as the down-regulated markers hsa-miR-103a-3p, hsa-miR-107,hsa-miR-532-5p, and hsa-let-7f-5p. X-axis: expression of AD samples vs.control determined by NGS, y-axis: expression of AD samples vs. controldetermined by qRT-PCR.

FIG. 8 shows the ROC curve for the best single miRNAs from thevalidation study, gene expression data obtained by qRT-PCR. X-axis:specificity, y-axis: sensitivity.

FIG. 9 shows the ROC curve for the 7-marker signature brain-mir-112,hsa-miR-5010-3p, hsa-miR-103a-3p, hsa-miR-107, hsa-let-7d-3p,hsa-miR-532-5p, and brain-mir-161, qRT-PCR. X-axis: specificity, y-axis:sensitivity.

FIG. 10 shows the improved combined score of controls (left column) vs.AD patients (right column).

FIG. 11 shows the validation of 12 miRNAs in 7 diseases (AD, MCI, PD,DEP, CIS, SCH, and BD and controls). The 12 miRNAs are (denoted bycolumns 1-12, respectively)hsa-let-7f-5p, hsa-miR-1285-5p, hsa-miR-107,hsa-miR-103a-3p, hsa-miR-26b-3p, hsa-miR-26a-5p, hsa-miR-532-5p,hsa-miR-151a-3p, brain-mir-161, hsa-let-7d-3p, brain-mir-112, andhsa-miR-5010-3p.

FIG. 12 shows the combined score of the 7-miRNA signature brain-mir-112,hsa-miR-5010-3p, hsa-miR-103a-3p, hsa-miR-107, hsa-let-7d-3p,hsa-miR-532-5p, and brain-mir-161 for all diseases. The combined score(y-axis) was obtained using quantitative RT PCR.

FIG. 13 shows a combined score of AD, MCI and controls for the 12-markersignature hsa-let-7f-5p, hsa-miR-1285-5p, hsa-miR-107, hsa-miR-103a-3p,hsa-miR-26b-3p, hsa-miR-26a-5p, hsa-miR-532-5p, hsa-miR-151a-3p,brain-mir-161, hsa-let-7d-3p, brain-mir-112, and hsa-miR-5010-3p. Thecombined score (y-axis) was obtained using high throughput sequencing.

FIG. 14 shows the ROC curve for the 12-marker signature of FIG. 13, geneexpression data obtained by NGS. X-axis: specificity, y-axis:sensitivity.

FIG. 15 shows the ROC curve for the 12-marker signature of FIG. 13, geneexpression data obtained by qRT-PCR. X-axis: specificity, y-axis:sensitivity.

FIG. 16 shows the combined score of the 12-miRNA signaturehsa-let-7f-5p, hsa-miR-1285-5p, hsa-miR-107, hsa-miR-103a-3p,hsa-miR-26b-3p, hsa-miR-26a-5p, hsa-miR-532-5p, hsa-miR-151a-3p,brain-mir-161, hsa-let-7d-3p, brain-mir-112, and hsa-miR-5010-3p for alldiseases. The combined score (y-axis) was obtained using quantitative RTPCR.

DETAILED DESCRIPTION OF THE INVENTION Definitions

Unless defined otherwise, technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs.

The term “predicting an outcome” of a disease, as used herein, is meantto include both a prediction of an outcome of a patient undergoing agiven therapy and a prognosis of a patient who is not treated.

An “outcome” within the meaning of the present invention is a definedcondition attained in the course of the disease. This disease outcomemay e.g. be a clinical condition such as “relapse of disease”,“remission of disease”, “response to therapy”, a disease stage or gradeor the like.

A “risk” is understood to be a probability of a subject or a patient todevelop or arrive at a certain disease outcome. The term “risk” in thecontext of the present invention is not meant to carry any positive ornegative connotation with regard to a patient's wellbeing but merelyrefers to a probability or likelihood of an occurrence or development ofa given event or condition.

The term “clinical data” relates to the entirety of available data andinformation concerning the health status of a patient including, but notlimited to, age, sex, weight, menopausal/hormonal status, etiopathologydata, anamnesis data, data obtained by in vitro diagnostic methods suchas blood or urine tests, data obtained by imaging methods, such asx-ray, computed tomography, MRI, PET, spect, ultrasound,electrophysiological data, genetic analysis, gene expression analysis,biopsy evaluation, intraoperative findings.

The term “classification of a sample” of a patient, as used herein,relates to the association of said sample with at least one of at leasttwo categories. These categories may be for example “high risk” and “lowrisk”; or high, intermediate and low risk; wherein risk is theprobability of a certain event occurring in a certain time period, e.g.occurrence of disease, progression of disease, etc. It can further meana category of favourable or unfavourable clinical outcome of disease,responsiveness or non-responsiveness to a given treatment or the like.Classification may be performed by use of an algorithm, in particular adiscrimant function. A simple example of an algorithm is classificationaccording to a first quantitative parameter, e.g. expression level of anucleic acid of interest, being above or below a certain thresholdvalue. Classification of a sample of a patient may be used to predict anoutcome of disease or the risk of developing a disease. Instead of usingthe expression level of a single nucleic acid of interest, a combinedscore of several nucleic acids of interest of interest may be used.Further, additional data may be used in combination with the firstquantitative parameter. Such additional data may be clinical data fromthe patient, such as sex, age, weight of the patient, disease gradingetc.

A “discriminant function” is a function of a set of variables used toclassify an object or event. A discriminant function thus allowsclassification of a patient, sample or event into a category or aplurality of categories according to data or parameters available fromsaid patient, sample or event. Such classification is a standardinstrument of statistical analysis well known to the skilled person.E.g. a patient may be classified as “high risk” or “low risk”, “in needof treatment” or “not in need of treatment” or other categoriesaccording to data obtained from said patient, sample or event.Classification is not limited to “high vs. low”, but may be performedinto a plurality of categories, grading or the like. Examples fordiscriminant functions which allow a classification include, but are notlimited to discriminant functions defined by support vector machines(SVM), k-nearest neighbors (kNN), (naive) Bayes models, or piecewisedefined functions such as, for example, in subgroup discovery, indecision trees, in logical analysis of data (LAD) an the like.

The term “expression level” refers, e.g., to a determined level ofexpression of a nucleic acid of interest. The term “pattern ofexpression levels” refers to a determined level of expression com-paredeither to a reference nucleic acid, e.g. from a control, or to acomputed average expression value, e.g. in DNA-chip analyses. A patternis not limited to the comparison of two genes but is also related tomultiple comparisons of genes to reference genes or samples. A certain“pattern of expression levels” may also result and be determined bycomparison and measurement of several nucleic acids of interestdisclosed hereafter and display the relative abundance of thesetranscripts to each other. Expression levels may also be assessedrelative to expression in different tissues, patients versus healthycontrols, etc.

A “reference pattern of expression levels”, within the meaning of theinvention shall be understood as being any pattern of expression levelsthat can be used for the comparison to another pattern of expressionlevels. In a preferred embodiment of the invention, a reference patternof expression levels is, e.g., an average pattern of expression levelsobserved in a group of healthy or diseased individuals, serving as areference group.

In the context of the present invention a “sample” or a “biologicalsample” is a sample which is derived from or has been in contact with abiological organism. Examples for biological samples are: cells, tissue,body fluids, biopsy specimens, blood, urine, saliva, sputum, plasma,serum, cell culture supernatant, and others.

A “probe” is a molecule or substance capable of specifically binding orinteracting with a specific biological molecule. The term “primer”,“primer pair” or “probe”, shall have ordinary meaning of these termswhich is known to the person skilled in the art of molecular biology. Ina preferred embodiment of the invention “primer”, “primer pair” and“probes” refer to oligonucleotide or polynucleotide molecules with asequence identical to, complementary too, homologues of, or homologousto regions of the target molecule or target sequence which is to bedetected or quantified, such that the primer, primer pair or probe canspecifically bind to the target molecule, e.g. target nucleic acid, RNA,DNA, cDNA, gene, transcript, peptide, polypeptide, or protein to bedetected or quantified. As understood herein, a primer may in itselffunction as a probe. A “probe” as understood herein may also comprisee.g. a combination of primer pair and internal labeled probe, as iscommon in many commercially available qPCR methods.

A “gene” is a set of segments of nucleic acid that contains theinformation necessary to produce a functional RNA product in acontrolled manner. A “gene product” is a biological molecule producedthrough transcription or expression of a gene, e.g. an mRNA or thetranslated protein.

A “miRNA” is a short, naturally occurring RNA molecule and shall havethe ordinary meaning understood by a person skilled in the art. A“molecule derived from an miRNA” is a molecule which is chemically orenzymatically obtained from an miRNA template, such as cDNA.

The term “array” refers to an arrangement of addressable locations on adevice, e.g. a chip device. The number of locations can range fromseveral to at least hundreds or thousands. Each location represents anindependent reaction site. Arrays include, but are not limited tonucleic acid arrays, protein arrays and antibody-arrays. A “nucleic acidarray” refers to an array containing nucleic acid probes, such asoligonucleotides, polynucleotides or larger portions of genes. Thenucleic acid on the array is preferably single stranded. A “microarray”refers to a biochip or biological chip, i.e. an array of regions havinga density of discrete regions with immobilized probes of at least about100/cm2.

A “PCR-based method” refers to methods comprising a polymerase chainreaction PCR. This is a method of exponentially amplifying nucleicacids, e.g. DNA or RNA by enzymatic replication in vitro using one, twoor more primers. For RNA amplification, a reverse transcription may beused as a first step. PCR-based methods comprise kinetic or quantitativePCR (qPCR) which is particularly suited for the analysis of expressionlevels). When it comes to the determination of expression levels, a PCRbased method may for example be used to detect the presence of a givenmRNA by (1) reverse transcription of the complete mRNA pool (the socalled transcriptome) into cDNA with help of a reverse transcriptaseenzyme, and (2) detecting the presence of a given cDNA with help ofrespective primers. This approach is commonly known as reversetranscriptase PCR (rtPCR). The term “PCR based method” comprises bothend-point PCR applications as well as kinetic/real time PCR techniquesapplying special fluorophors or intercalating dyes which emitfluorescent signals as a function of amplified target and allowmonitoring and quantification of the target. Quantification methodscould be either absolute by external standard curves or relative to acomparative internal standard.

The term “next generation sequencing” or “high throughput sequencing”refers to high-throughput sequencing technologies that parallelize thesequencing process, producing thousands or millions of sequences atonce. Examples include Massively Parallel Signature Sequencing (MPSS)Polony sequencing, 454 pyrosequencing, Illumina (Solexa) sequencing,SOLiD sequencing, Ion semiconductor sequencing, DNA nanoball sequencing,Helioscope™ single molecule sequencing, Single Molecule SMR™ sequencing,Single Molecule real time (RNAP) sequencing, Nanopore DNA sequencing.

The term “marker” or “biomarker” refers to a biological molecule, e.g.,a nucleic acid, peptide, protein, hormone, etc., whose presence orconcentration can be detected and correlated with a known condition,such as a disease state, or with a clinical outcome, such as response toa treatment.

In particular, the invention relates to a method of classifying a sampleof a patient suffering from or at risk of developing Alzheimer'sDisease, wherein said sample is a blood sample, said method comprisingthe steps of:

a) determining in said sample an expression level of at least one miRNAselected from the group consisting of miRNAs having the sequence SEQ IDNO 59, SEQ ID NO 65, SEQ ID NO 1 to SEQ ID NO 58, SEQ ID NO 60 to SEQ IDNO 64 and SEQ ID NO 66 to SEQ ID NO 170,b) comparing the pattern of expression level(s) determined in step a)with one or several reference pattern(s) of expression levels; andc) classifying the sample of said patient from the outcome of thecomparison in step b) into one of at least two classes.

A reference pattern of expression levels may, for example, be obtainedby determining in at least one healthy subject the expression level ofat least one miRNA selected from the group consisting of miRNAs havingthe sequence SEQ ID NO 59, SEQ ID NO 65, SEQ ID NO 1 to SEQ ID NO 58,SEQ ID NO 60 to SEQ ID NO 64 and SEQ ID NO 66 to SEQ ID NO 170.

It is within the scope of the invention to assign a numerical value toan expression level of the at least one miRNA determined in step a).

It is further within the scope of the invention to mathematicallycombine expression level values to obtain a pattern of expression levelsin step (b), e.g. by applying an algorithm to obtain a normalizedexpression level relative to a reference pattern of expression level(s).

In a further aspect the invention relates to a method for diagnosingAlzheimer's Disease, predicting risk of developing Alzheimer's Disease,or predicting an outcome of Alzheimer's Disease in a patient sufferingfrom or at risk of developing Alzheimer's Disease, said methodcomprising the steps of:

a) determining in a blood sample from said patient, the expression levelof at least one miRNA selected from the group consisting of miRNAs withthe sequence SEQ ID NO 59, SEQ ID NO 65, SEQ ID NO 1 to SEQ ID NO 58,SEQ ID NO 60 to SEQ ID NO 64 and SEQ ID NO 66 to SEQ ID NO 170,b) comparing the pattern of expression level(s) determined in step a)with one or several reference pattern(s) of expression levels; andc) diagnosing Alzheimer's Disease, predicting a risk of developingAlzheimer's Disease, or predicting an outcome of Alzheimer's Diseasefrom the outcome of the comparison in step b).

According to an aspect of the invention, said at least one miRNA isselected from the group consisting of miRNAs with the sequence SEQ ID NO59, SEQ ID NO 65, SEQ ID NO 1 and SEQ ID NO 56.

According to an aspect of the invention, step a) comprises determiningthe expression level of the miRNAs: brain-mir-112, hsa-miR-5010-3p,hsa-miR-103a-3p, hsa-miR-107, hsa-let-7d-3p, hsa-miR-532-5p, andbrain-mir-161.

According to an aspect of the invention, step a) comprises in step a)determining the expression level of 5 miRNAs selected from thesignatures consisting of

brain-mir-112 hsa-miR-5010-3p hsa-miR-1285-5p hsa-miR-151a-3phsa-let-7f-5p, hsa-miR-3127-3p hsa-miR-1285-5p hsa-miR-425-5p hsa-miR-148b-5p hsa-miR-144-5p, hsa-miR-3127-3p hsa-miR-3157-3p hsa-miR-148b-5phsa-miR-151a-3p hsa-miR-144-5p, hsa-miR-3127-3p hsa-miR-1285-5phsa-miR-425-5p hsa-miR- 151a-3p hsa-miR-144-5p, hsa-miR-1285-5pbrain-mir-112 hsa-miR-5010-3p hsa-miR- 151a-3p hsa-let-7a-5p,hsa-miR-5001-3p hsa-miR-1285-5p hsa-miR-425-5p hsa-miR- 148b-5phsa-miR-144-5p, hsa-miR-3127-3p hsa-miR-1285-5p hsa-miR-148b-5phsa-miR-151a-3p hsa-miR-144-5p, hsa-miR-1285-5p hsa-miR-5010-3phsa-miR-425-5p hsa-miR- 148b-5p hsa-miR-144-5p, hsa-miR-1285-5phsa-miR-5010-3p hsa-miR-151a-3p hsa-miR-144-5p hsa-let-7a-5p,hsa-miR-1285-5p brain-mir-112 hsa-miR-425-5p hsa-miR-151a- 3phsa-miR-144-5p, hsa-miR-5001-3p hsa-miR-1285-5p brain-mir-112 hsa-miR-151a-3p hsa-let-7f-5p, hsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-148b-5phsa-miR-144-5p hsa-let-7f-5p, hsa-miR-1285-5p hsa-miR-3157-3phsa-miR-148b-5p hsa-miR-151a-3p hsa-miR-144-5p, hsa-miR-5001-3phsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-151a-3p hsa-let-7f-5p,brain-mir-431 hsa-miR-1285-5p hsa-miR-3157-3p hsa-miR-151a-3phsa-miR-144-5p, hsa-miR-3127-3p hsa-miR-1285-5p brain-mir-112 hsa-miR-425-5p hsa-miR-151a-3p, hsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-151a-3phsa-miR-144-5p hsa-let-7f-5p, hsa-miR-550a-5p hsa-miR-1285-5pbrain-mir-112 hsa-miR- 151a-3p hsa-let-7f-5p, hsa-miR-1285-5pbrain-mir-112 hsa-miR-148b-5p hsa-miR- 151a-3p hsa-miR-144-5p, andhsa-miR-5001-3p brain-mir-112 hsa-miR-5010-3p hsa-miR- 151a-3phsa-let-7f-5p.

According to an aspect of the invention, the expression levels of aplurality of miRNAs are determined as expression level values and step(b) comprises mathematically combining the expression level values ofsaid plurality of miRNAs.

It is within the scope of the invention to apply an algorithm to thenumerical value of the expression level of the at least one miRNAdetermined in step a) to obtain a disease score to allow classificationof the sample or diagnosis, prognosis or prediction of the risk ofdeveloping Alzheimer's Disease, or prediction of an outcome ofAlzheimer's Disease. A non-limiting example of such an algorithm is tocompare the numerical value of the expression level against a thresholdvalue in order to classify the result into one of two categories, suchas high risk/low risk, diseased/healthy or the like. A furthernon-limiting example of such an algorithm is to combine a plurality ofnumerical values of expression levels, e.g. by summation, to obtain acombined score. Individual summands may be normalized or weighted bymultiplication with factors or numerical values representing theexpression level of an miRNA, numerical values representing clinicaldata, or other factors.

It is within the scope of the invention to apply a discriminant functionto classify a result, diagnose disease, predict an outcome or a risk.

According to an aspect of the invention, the expression level in step(a) is obtained by use of a method selected from the group consisting ofa Sequencing-based method, an array based method and a PCR-based method.

According to an aspect of the invention, the expression levels of atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 miRNAs are determined toobtain a pattern of expression levels.

According to an aspect of the invention, step a) comprises in step a)determining the expression level of the miRNAs: hsa-let-7f-5p,hsa-miR-1285-5p, hsa-miR-107, hsa-miR-103a-3p, hsa-miR-26b-3p,hsa-miR-26a-5p, hsa-miR-532-5p, hsa-miR-151a-3p, brain-mir-161,hsa-let-7d-3p, brain-mir-112, and hsa-miR-5010-3p.

The invention further relates to a kit for performing the methods of theinvention, said kit comprising means for determining in said bloodsample from said patient, an expression level of at least one miRNAselected from the group consisting of miRNAs with the sequence SEQ ID NO59, SEQ ID NO 65, SEQ ID NO 1 to SEQ ID NO 58, SEQ ID NO 60 to SEQ ID NO64 and SEQ ID NO 66 to SEQ ID NO 170.

The means for determining the expression level of said at least onemiRNA may comprise an oligonucleotide probe for detecting or amplifyingsaid at least one miRNA, means for determining the expression levelbased on an array-based method, a PCR-based method, a sequencing-basedmethod or any other suitable means for determining the expression level.

According to an aspect of the invention, the kit further comprises atleast one reference pattern of expression levels for comparing with theexpression level of the at least one miRNA from said sample. Thereference pattern of expression may include at least one digital ornumerical information and may be provided in any readable orelectronically readable form, including, but not limited to printedform, electronically stored form on a computer readable medium, such asCD, smart card, or provided in downloadable form, e.g. in a computernetwork such as the internet.

The invention further relates to computer program product useful forperforming the methods of the invention, comprising

-   -   means for receiving data representing an expression level of at        least one miRNA in a patient blood sample selected from the        group consisting of miRNAs with the sequence SEQ ID NO 59, SEQ        ID NO 65, SEQ ID NO 1 to SEQ ID NO 58, SEQ ID NO 60 to SEQ ID NO        64 and SEQ ID NO 66 to SEQ ID NO 170,    -   means for receiving data representing at least one reference        pattern of expression levels for comparing with the expression        level of the at least one miRNA from said sample,    -   means for comparing said data representing the expression level        of the at least one miRNA in a patient sample, and    -   optionally means for determining a diagnosis of Alzheimer's        Disease, a prediction of a risk of developing Alzheimer's        Disease, or a prediction of an outcome of Alzheimer's Disease        from the outcome of the comparison in step b).

The computer program product may be provided on a storable electronicmedium, such as a solid state memory, disk, CD or other. It may bestored locally on a computer. It may be implemented as network-basedprogram or application, including a web- or internet-based application.It may be implemented in a diagnostic device, such as an analyzerinstrument. It may be operably connected to a device for outputtinginformation, such as a display, printer or the like.

EXAMPLES

Additional details, features, characteristics and advantages of theobject of the invention are further disclosed in the followingdescription and figures of the respective examples, which, in anexemplary fashion, show preferred embodiments of the present invention.However, these examples should by no means be understood as to limit thescope of the invention.

The invention relates to methods for diagnosing Alheimer's Disease withmiRNA markers.

Diagnosis of Alzheimer's Disease can be challenging in patientspresenting with generally age-related syndromes such as forgetfulness.In particular, it is difficult to diagnose the earliest stages ofdisease. However, it would be particularly desirable to have a reliablediagnostic test for this stage of disease, as the chance of therapeuticand social intervention is better during this early disease stage.

Here, the abundance of miRNAs in blood samples of Alzheimer's Diseasepatients has been compared in an unbiased approach against healthycontrols and patients suffering from other neuronal disorders. Thisapproach involved a massive effort of sequencing miRNAs from samples andthus was open to the discovery of novel markers not yet described in theprior art. Further, the use of blood samples as a source of expressioninformation of miRNA markers has several tangible advances which are notavailable in other sample sources such as serum or tissue, such as easeof sample procurement and handling, sample preparation, and robustnessand consistency of expression patterns.

Materials and Methods Patient Cohorts

The expression of miRNAs in peripheral blood of a total of 219 patientsand healthy controls was determined, either by NGS or by qRT-PCR orboth. Blood was obtained from patients with Alzheimer's Disease (AD)(n=106), patients with Mild Cognitive Impairement (MCI) (n=21), patientswith Multiple Sclerosis (Clinically Isolated Syndrome, CIS) (n=17),patients with Parkinson's Disease (PD) (n=9), patients with MildDepression (DEP) (n=15), Bipolar Disorder (BD) (n=15), Schizophrenia(Schiz) (n=14), and from healthy controls (n=22).

First, samples from AD patients (n=48), MCI patients (n=20) and healthycontrols (n=22) were analyzed by Next-generation sequencing. Forvalidation purposes the expression of single miRNAs was analyzed usingqRT-PCR in the same samples as used for NGS, if enough RNA wasavailable. The number of samples was further expanded by further samplesfrom patients with AD, CIS, PD, DEP, BD, and Schiz, resulting in a totalof 205 samples analyzed by qRT-PCR. In detail, a total of 95 samplesfrom AD patients, 19 samples from MCI patients, 17 samples from CISpatients, 9 samples from PD patients, 15 samples from DEP patients, 15samples from BD patients, 14 samples from Schiz patients, and 21 samplesfrom healthy controls were analyzed.

RNA Isolation

Total RNA including miRNA was isolated using the PAXgene Blood miRNA Kit(Qiagen) following the manufacturer's recommendations. Isolated RNA wasstored at −80° C. RNA integrity was analyzed using Bioanalyzer 2100(Agilent) and concentration and purity were measured using NanoDrop 2000(Thermo Scientific). A total of four samples (three controls and oneRRMS) failed the quality criteria and were excluded from the study.

Library Preparation and Next-Generation Sequencing

For the library preparation, 200 ng of total RNA was used per sample, asdetermined with a RNA 6000 Nano Chip on the Bioanalyzer 2100 (Agilent).Preparation was performed following the protocol of the TruSeq Small RNASample Prep Kit (Illumina). Concentration of the ready prepped librarieswas measured on the Bioanalyzer using the DNA 1000 Chip. Libraries werethen pooled in batches of six samples in equal amounts and clusteredwith a concentration of 9 pmol in one lane each of a single readflowcell using the cBot (Illumina). Sequencing of 50 cycles wasperformed on a HiSeq 2000 (Illumina). Demultiplexing of the rawsequencing data and generation of the fastq files was done using CASAVAv.1.8.2.

NGS Data Analysis

The raw illumina reads were first preprocessed by cutting the 3′ adaptersequence using the programm fastx_clipper from the FASTX-Toolkit(http://hannonlab.cshl.edu/fastx_toolkit/). Reads shorter than 18 ntsafter clipping were removed. The remaining reads are reduced to uniquereads and their frequency per sample to make the mapping steps more timeefficient. For the remaining steps, we used the miRDeep2 pipeline. Thesesteps consist of mapping the reads against the genome (hg19), mappingthe reads against miRNA precursor sequences from mirbase release v18,summarizing the counts for the samples, and the prediction of novelmiRNAs. Since the miRDeep2 pipeline predicts novel miRNAs per sample,the miRNAs were merged afterwards as follows: first, the novel miRNAsper sample that have a signal-to-noise ratio of more than 10 wereextracted. Subsequently, only those novel miRNAs that are located on thesame chromosome were merged, and both their mature forms share anoverlap of at least 11 nucleotides.

Quantitative Real Time-PCR (qRT-PCR)

Out of the NGS results 7 miRNAs were selected that were deregulated inboth, the comparison between patients with Alzheimer's Disease andpatients with Mild Cognitive Impairment, and the comparison betweenpatients with Alzheimer's Disease and healthy individuals. Five of theseven miRNAs, namely hsa-miR-5010-3p, hsa-miR-103a-3p, hsa-miR-107,hsa-let-7d-3p, and hsa-miR-532-5p were already known mature miRNAsincluded in miRBase, two miRNAs, namely brain-mir-112 and brain-mir-161,were newly identified and not yet included in miRBase. As endogenouscontrol the small nuclear RNA RNU48 as used.

The miScript PCR System (Qiagen) was used for reverse transcription andqRT-PCR. A total of 200 ng RNA was converted into cDNA using themiScript Reverse Transcription Kit according to the manufacturers'protocol. For each RNA we additionally prepared 5 μl reactionscontaining 200 ng RNA and 4 μl of the 5× miScript RT Buffer but nomiScript Reverse Transcriptase Mix, as negative control for the reversetranscription (RT− control). The qRT-PCR was performed with the miScriptSYBR® Green PCR Kit in a total volume of 20 μl per reaction containing 1μl cDNA according to the manufacturers' protocol. For each miScriptPrimer Assay we additionally prepared a PCR negative-control with waterinstead of cDNA (non-template control, NTC).

Bioinformatics Analysis

First the read counts were normalized using standard quantilenormalization. All miRNAs with less than 50 read counts were excludedfrom further considerations. Next, we calculated for each miRNA the areaunder the receiver operator characteristic curve (AUC), the fold-change,and the significance value (p-value) using t-tests. All significancevalues were adjusted for multiple testing using the Benjamini Hochbergapproach. The bioinformatics analyses have been carried out using thefreely available tool. R. Furthermore, we carried out a miRNA enrichmentanalysis using the TAM tool (http://202.38.126.151/hmdd/tools/tam.html).

Computing Combined Scores

Briefly, to compute a combined expression score for n up-regulatedmarkers and m down-regulated markers the difference d between theexpression value x_((a)) of a patient a and the average expression valueof all controls μ is determined. For down-regulated markers, thedifference can be multiplied by (−1), thus yielding a positive value.The differences for n markers can be added up to yield a combined scoreZ, such that

Z _((a)) =Σd _((1-n))(upregulated)+Σ(−1)d _((1-m))(down-regulated)

Wherein

d=x _((a))−μ

To make combined scores between different marker scores comparable (e.g.to compare a (n+m)=7 marker score against a (n+m)=12 marker score, thecombined score can be divided by (n+m):

Zcomp=1/(n+m)(Σd _((1-n))(upregulated)+Σ(−1)d _((1-m))(down-regulated))

Other factors can be applied to the individual summands d of thecombined score or the combined score Z as a whole.

Results Screening Using High-Throughput Sequencing

To detect potential Alzheimer biomarkers a high-throughput sequencing ofn=22 controls samples (C), n=48 Alzheimer patient (AD) samples and n=20Mild Cognitive Impairment (MCI) samples was carried out. Precisely,Illumina HiSeq 2000 sequencing and multiplexed 8 samples on eachsequencing lane was carried out. Thereby, 1150 of all human maturemiRNAs in at least a single sample could be detected.

TABLE 1 Patient Cohorts Cohort Size Cohort Size Disease ScreeningReplication Controls 22 21 Alzheimer 48 86 (US) Alzheimer 0 9 (GER)Parkinson 0 9 Disease Mild 20 18 Cognitive Impairment Schizophrenia 0 14Bipolar 0 15 disease Multiple 0 17 Sclerosis (CIS) Depression 0 15 SUM90 204

The most abundant miRNAs were hsa-miR-486-5p with an average read-countof 13,886,676 and a total of 1.2 billion reads mapping to this miRNA,hsa-miR-92a-3p with an average of 575,359 reads and a total of 52million reads mapping to this miRNA and miR-451a with an average of135,012 reads and a total of 12 million reads mapping to this miRNA. Thedistribution of reads mapping to the three most abundant and all othermiRNAs is shown in FIG. 1 (left pie chart). Additionally, 548 novelmature miRNA candidates were detected that have been previously notpresent in the Sanger miRBase. These miRNA candidates have generallyhowever been much less abundant as compared to the known human miRNAs.The most abundant one, denoted as brain-miR-314 was detected on averagewith 3,587 reads per sample and a total of 322,868 reads. Second highestexpressed miRNA, brain-miR-247 was present on average with 3,112 andwith a total of 280,115 reads, third most abundant miRNA brain-miR-12 atan average of 2,630 and a total of 236,728 reads. In the list of all,novel and known miRNAs, brain-miR-314 would be ranked on position 37,i.e., 36 known human miRNAs were more abundant than the highest abundantnovel one. While a total of 1.4 Bn reads mapped to the known miRNAs,only 2.3 Mn mapped to the novel miRNA candidates. This relation showsthat a very high sequencing capacity is required to reach thesensitivity in order to detect rare variants of novel miRNAs in humanblood samples. Interestingly, as the right pie chart in FIG. 1 denotes,the candidate miRNAs are much more equally distributed as compared tothe known ones, where the most abundant miRNA was responsible for 91% ofall reads.

To detect potential biomarker candidates two-tailed t-tests and adjustedthe significance values for multiple testing using Benjamini Hochbergadjustment were computed. All markers with adjusted significance valuesbelow 0.05 were considered statistically significant. Additionally, thearea under the receiver operator characteristics curve (AUC) wascomputed to understand the specificity and sensitivity of miRNAs forAlzheimer diagnosis. Altogether, 170 significantly dys-regulated miRNAswe detected, 55 markers were significantly down-regulated in Alzheimer,while 115 were significantly up-regulated. A list of the respective 170markers is presented in Supplemental Table 1 a and b. These 170 miRNAmarkers have the corresponding sequences SEQ ID NO 1 to SEQ ID NO 170 inthe attached sequence protocol.

A list of all miRNA molecules described herein is given in SupplementalTable 4 containing an overview of the miRNA markers, including sequenceinformation.

It is noted that the mature miRNa originate from miRNA precursormolecules of length of around 120 bases. Several examples exists wherethe miRNA precursors vary from each other while the subset of the around20 bases belonging to the mature miRNA are identical. Thus, novel maturemiRNAs can have the same sequence but different SEQ ID NO identifiers.

MiRNA markers are denoted by their common name (e.g. has-miR-144-5p orhsa-let 7f-5p) and are searchable in publically available databases. Inthis invention there are also described novel miRNA markers which havebeen named with names beginning with the prefix “brain-miR”. They arelisted in supplemental table 2 with their sequence and their SEQ ID NOaccording to the sequence protocol.

The ROC curves for the most up-regulated marker (hsa-miR-30d-5p withp-value of 8*10⁻⁹) as well as the most down-regulated marker(hsa-miR-144-5p with p-value of 1.5*10⁻⁵) are presented in FIGS. 2 and3, where the high AUC value indicates that already one single miRNAmight have sufficient power to differentiate between cases and controls.Both miRNAs have however already been describe with many other humanpathologies including different neoplasms and thus are non-specific forAD. Remarkably, the set of significant biomarkers also contained also 58miRNAs that had so far not been reported, which have been designatedwith miRNA Names beginning with. Of these, only 10 were down-regulatedwhile the majority of 48 miRNAs was highly up-regulated in AD.

To understand whether the detected biomarkers are also dys-regulated inMCI patients t-tests and AUC values for the comparison of healthycontrols versus MCI were likewise computed. Here, ten markers remainedstatistically significant following adjustment for multiple testing. Ofthese, 8 were down-while 2 were up-regulated in MCI patients. Notably, 9of them have been likewise significantly dys-regulated in MCI patients,namely hsa-miR-29c-3p, hsa-miR-29a-3p, hsa-let-7e-5p, hsa-let-7a-5p,hsa-let-7f-5p, hsa-miR-29b-3p, hsa-miR-98, hsa-miR-425-5p andhsa-miR-181a-2-3p. Only miRNA hsa-miR-223-3p was just significant in MCIpatients while not in AD patients. A full list of all MCI biomarkers,identified as SEQ ID NO 171-235 in the attached sequence listing ispresented in Supplemental Table 3. It is noted that mature miRNAoriginate from miRNA precursor molecules of length of around 120 bases.Several examples exists where the miRNA precursors vary from each otherwhile the subset of the around 20 bases belonging to the mature miRNAare identical. Thus, novel mature miRNAs can have the same sequence butdifferent identifiers.

Besides single markers, combinations of multiple markers havedemonstrated a potential to improve the diagnostic accuracy. To testthis hypothesis, a standard machine learning approach was applied. In across-validation loop, the markers with lowest significance values werestepwise added and repeatedly radial basis function support vectormachines were carried out. The accuracy, specificity and sensitivitydepend on the number of biomarkers are presented in FIG. 4. As shownthere, accuracy, specificity and sensitivity increase up to a signaturenumber of 250 miRNAs and then converge to 90%. However, this set ofmiRNAs contains a significant amount of redundant biomarkers, i.e.,markers that have almost identical information content to each other andare highly correlated such that even significantly smaller sets ofmarkers can be expected to perform highly accurate distinction inAlzheimer samples and controls. We selected a signature of just 7markers, namely the up-regulated miRNAs brain-mir-112, brain-mir-161,hsa-let-7d-3p and hsa-miR-5010-3p as well as the down-regulated markershsa-miR-103a-3p, hsa-miR-107 and hsa-miR-532-5p. To combine the valuesof the 7 miRNAs in one score we calculated the average z-score asdetailed in the Material & Methods section. While we reached averagedvalues of 0.087 and standard deviation of 0.72 for the controls andaverage values of 0.22 and standard deviation of 0.74 for the MCIpatients, AD patients reached a much higher score of 0.63 at a standarddeviation of 0.64. Thus, the Alzheimer patients have significantlyhigher scores as controls, indicated by the two-tailed t-test p-value of0.025. These numbers are detailed as bar-chart in FIG. 5. The ROC curvefor the signature showing an AUC of 84.3% with 95% CI of 75.3%-93.2% ispresented in FIG. 6.

A further signature of 12 markers with limited cross-correlation wasselected, including the most strongly dys-regulated markers that areless frequently dys-regulated in other diseases and show a potential toseparate AD also from MCI. More precisely, this selected signaturecontains the up-regulated miRNAs brain-mir-112, brain-mir-161,hsa-let-7d-3p, hsa-miR-5010-3p, hsa-miR-26b-3p, hsa-miR-26a-5p,hsa-miR-1285-5p, and hsa-miR-151a-3p as well as the down-regulatedmarkers hsa-miR-103a-3p, hsa-miR-107, hsa-miR-532-5p, and hsa-let-7f-5p.To combine the values of the 12 miRNAs in one score the combined scorewas computed as discussed above. While averaged values of 0 and standarddeviation of 0.39 for the controls were reached and average values of0.32 and standard deviation of 0.5 for the MCI patients were reached, ADpatients reached a much higher score of 0.93 at a standard deviation of0.54. Thus, the Alzheimer patients have significantly higher scores ascontrols, indicated by the two-tailed t-test p-value of 3.7*10⁻¹¹ incase of AD versus C as well as 6*10⁻⁵ in case of AD versus MCI. Inaddition we computed the same scores for a set of 15 MS samples, showinga likewise decreased score of 0.1 at standard deviation of 0.34.

Biological Relevance of miRNAs for AD

To understand the biological function of the dys-regulated miRNAs bettera miRNA enrichment analysis for the up- and down-regulated miRNAs wasapplied (Ming Lu, Bing Shi, Juan Wang, Qun Cao and Qinghua Cui. TAM: Amethod for enrichment and depletion analysis of a microRNA category in alist of microRNAs. BMC Bioinformatics 2010, 11:419 (9 Aug. 2010). Theresults of this analysis are detailed in Table 2. Altogether, for the 55down-regulated miRNAs 11 significant categories after adjustment formultiple testing were detected while for the 115 up-regulated just asingle category remained significant, the miR-30 family with 5 membersbeing up-regulated. In contrast, for the down-regulated miRNAs 7 miRNAsof the let-7 family were found being significant. In addition, the setcontained also 8 miRNAs belonging to anti-cell proliferation and 13tumor suppressors. Finally, we were able to show that the down-regulatedmiRNAs correlate to 8 diseases, including Alzheimer. Here, we found 5miRNAs being relevant, including hsa-miR-17, hsa-miR-29a, hsa-miR-29b,hsa-miR-106b and hsa-miR-107.

TABLE 2 Regulated Pathways and categories down up Term Count p-valueCount p-value anti-cell proliferation 8 4.60-3 n.s. n.s. miRNA tumorsuppressors 13 6.71-3 n.s. n.s. let-7 family 7 7.00-3 n.s. n.s.Digestive System 6 0.0144 n.s. n.s. Neoplasms Pituitary Neoplasms 70.0168 n.s. n.s. Lymphoma, Primary 7 0.0201 n.s. n.s. Effusion Sarcoma,Kaposi 7 0.021 n.s. n.s. Carcinoma, Non-Small Cell 6 0.027 n.s. n.s.Lung Neoplasms 14 0.028 n.s. n.s. Colonic Neoplasms 12 0.0388 n.s. n.s.Alzheimer Disease 5 0.0433 n.s. n.s. mir-30 family n.s. n.s. 5 8.95-3Validation of Signature by q-RT-PCR

In order to transfer the signature to clinical routine settings it isessential that the proposed in-vitro diagnostic test can be applied inmolecular diagnostic labs in reasonable time using standard equipment.To this end, qRT-PCR represents a suitable solution to replicate andvalidate our AD signature using this approach. In addition to measurejust controls, AD and MCI patients, a wide range of other neurologicaldisorders were also included. For AD, besides the US cohort also a setof samples collected in Germany were included. The full overview onmeasured samples is provided in Table 1.

First, the fold quotients of the initial screening cohort were comparedand analyzed by next-generation sequencing and this was compared to theperformance of the same miRNAs by qRT-PCR. As the scatter-plot in FIG. 7presents, all miRNAs have been dys-regulated in the same direction byboth approaches and in both cohorts, indicating a very high degree ofconcordance between screening and validation study. As for the nextgeneration sequencing screening approach AUC values were calculated forthe validation qRT-PCR cohort. The best single miRNA was miR-5010-3pwith an AUC of 84.5% (AUC of screening: 75.5%). On average, the 7 miRNAsreached an AUC value of 71%, indicating the high diagnostic informationcontent. Next, the question was addressed whether the combination of the7 miRNAs can further improve the diagnosis of AD. The same z-scoredbased approach was applied.

While averaged values of 0.087 and standard deviation of 0.72 for thecontrols and average values of 0.22 and standard deviation of 0.74 werereached for the MCI patients, AD patients reached a much higher score of0.63 at a standard deviation of 0.64.

For controls an average value of 0 (screening: −0.087) at a standarddeviation of 0.34 (screening: 0.72) was obtained, while for AD patients,the score was as high as 0.7 (screening 0.63) at standard deviation of0.45 (screening: 0.64). Thus, AD patients have significantly highervalues as compared to controls since the 2-tailed t-test p-value is aslow as 1.3*10-9 (screening 0.025). The z-scores are presented asbar-diagram in FIG. 10. Here, it can be clearly seen that especially thestandard deviations are much smaller for the qRT-PCR based validationcohort.

Scores of Other Neurological Disorders

Next the question was asked whether a cohort of other neurologicaldisorders shows likewise significant deviations to controls. As detailedin Table 1 we measured a second cohort of Alzheimer patients, Parkinsondisease, mild cognitive impairment, schizophrenia, bipolar disorder,multiple sclerosis (CIS) depression patients for the signature of 7miRNAs. In FIG. 11, the bar diagrams for all diseases and all miRNAs arepresent. Here, the Alzheimer patients score is set to 0, as describedearlier we have four down- and three up-regulated miRNAs for thecontrols. For mild cognitive impairment patients the same four miRNAswere down- and the same three miRNAs were up-regulated, providing strongevidence that the MCI signature is much closer to controls as comparedto AD. For CIS patients only two miRNAs were down-regulated, while thethird one was not dys-regulated and the remaining three were stronglyup-regulated. For Parkinson disease, the first 5 miRNAs were down-whilethe remaining two were strongly up-regulated. For Schizophrenia andBipolar Disease, almost all miRNAs were strongly up-regulated, incontrast, for Depression all miRNAs were significantly down-regulated.In summary, the results promise that AD can not only be distinguishedfrom controls but also very well from other neurological disorders. Ofcourse the same z-score based approach can be applied as for theAlzheimer and control patients in order to get an overall score for eachcohort.

Further significant signatures of miRNA for differentiating between ADand controls have been found:

-   -   hsa-miR-1285-5p brain-mir-112 hsa-miR-5010-3p hsa-miR-151a-3p        hsa-let-7f-5p,    -   hsa-miR-3127-3p hsa-miR-1285-5p hsa-miR-425-5p hsa-miR-148b-5p        hsa-miR-144-5p,    -   hsa-miR-3127-3p hsa-miR-3157-3p hsa-miR-148b-5p hsa-miR-151a-3p        hsa-miR-144-5p,    -   hsa-miR-3127-3p hsa-miR-1285-5p hsa-miR-425-5p hsa-miR-151a-3p        hsa-miR-144-5p,    -   hsa-miR-1285-5p brain-mir-112 hsa-miR-5010-3p hsa-miR-151a-3p        hsa-let-7a-5p,    -   hsa-miR-5001-3p hsa-miR-1285-5p hsa-miR-425-5p hsa-miR-148b-5p        hsa-miR-144-5p,    -   hsa-miR-3127-3p hsa-miR-1285-5p hsa-miR-148b-5p hsa-miR-151a-3p        hsa-miR-144-5p,    -   hsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-425-5p hsa-miR-148b-5p        hsa-miR-144-5p,    -   hsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-151a-3p hsa-miR-144-5p        hsa-let-7a-5p,    -   hsa-miR-1285-5p brain-mir-112 hsa-miR-425-5p hsa-miR-151a-3p        hsa-miR-144-5p,    -   hsa-miR-5001-3p hsa-miR-1285-5p brain-mir-112 hsa-miR-151a-3p        hsa-let-7f-5p,    -   hsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-148b-5p hsa-miR-144-5p        hsa-let-7f-5p,    -   hsa-miR-1285-5p hsa-miR-3157-3p hsa-miR-148b-5p hsa-miR-151a-3p        hsa-miR-144-5p,    -   hsa-miR-5001-3p hsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-151a-3p        hsa-let-7f-5p,    -   brain-mir-431 hsa-miR-1285-5p hsa-miR-3157-3p hsa-miR-151a-3p        hsa-miR-144-5p,    -   hsa-miR-3127-3p hsa-miR-1285-5p brain-mir-112 hsa-miR-425-5p        hsa-miR-151a-3p,    -   hsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-151a-3p hsa-miR-144-5p        hsa-let-7f-5p,    -   hsa-miR-550a-5p hsa-miR-1285-5p brain-mir-112 hsa-miR-151a-3p        hsa-let-7f-5p,    -   hsa-miR-1285-5p brain-mir-112 hsa-miR-148b-5p hsa-miR-151a-3p        hsa-miR-144-5p, and    -   hsa-miR-5001-3p brain-mir-112 hsa-miR-5010-3p hsa-miR-151a-3p        hsa-let-7f-5p.

These are further preferred combinations for classifying a sample of apatient suffering from or at risk of developing Alzheimer's Disease ordiagnosing AD, or predicting an outcome of AD (ca. Table 3)

TABLE 3 Further preferred signatures for diagnosing AD. Mean mean meanmean AD Signature AUC AD Control MCI replication miRNA 1 miRNA 2 miRNA 3miRNA 4 miRNA 5 sig #1 0.011 1.123 −0.019 0.557 1.190 hsa-miR-brain-mir- hsa-miR- hsa-miR- hsa-let- 1285-5p 112 5010-3p 151a-3p 7f-5psig #2 0.011 1.054 −0.012 0.549 1.281 hsa-miR- hsa-miR- hsa-miR-hsa-miR- hsa-miR- 3127-3p 1285-5p 425-5p 148b-5p 144-5p sig #3 0.0151.101 −0.028 0.454 1.137 hsa-miR- hsa-miR- hsa-miR- hsa-miR- hsa-miR-3127-3p 3157-3p 148b-5p 151a-3p 144-5p sig #4 0.015 1.097 −0.015 0.6631.325 hsa-miR- hsa-miR- hsa-miR- hsa-miR- hsa-miR- 3127-3p 1285-5p425-5p 151a-3p 144-5p sig #5 0.016 1.111 −0.020 0.561 1.187 hsa-miR-brain-mir- hsa-miR- hsa-miR- hsa-let- 1285-5p 112 5010-3p 151a-3p 7a-5psig #6 0.018 1.078 0.003 0.515 1.318 hsa-miR- hsa-miR- hsa-miR- hsa-miR-hsa-miR- 5001-3p 1285-5p 425-5p 148b-5p 144-5p sig #7 0.020 1.097 −0.0150.490 1.140 hsa-miR- hsa-miR- hsa-miR- hsa-miR- hsa-miR- 3127-3p 1285-5p148b-5p 151a-3p 144-5p sig #8 0.020 1.062 −0.010 0.493 1.299 hsa-miR-hsa-miR- hsa-miR- hsa-miR- hsa-miR- 1285-5p 5010-3p 425-5p 148b-5p144-5p sig #9 0.021 1.152 0.002 0.645 1.332 hsa-miR- hsa-miR- hsa-miR-hsa-miR- hsa-let- 1285-5p 5010-3p 151a-3p 144-5p 7a-5p sig #10 0.0211.139 −0.014 0.614 1.217 hsa-miR- brain-mir- hsa-miR- hsa-miR- hsa-miR-1285-5p 112 425-5p 151a-3p 144-5p sig #11 0.021 1.139 −0.006 0.579 1.209hsa-miR- hsa-miR- brain-mir- hsa-miR- hsa-let- 5001-3p 1285-5p 112151a-3p 7f-5p sig #12 0.021 1.120 0.006 0.527 1.291 hsa-miR- hsa-miR-hsa-miR- hsa-miR- hsa-let- 1285-5p 5010-3p 148b-5p 144-5p 7f-5p sig #130.021 1.111 −0.015 0.400 1.031 hsa-miR- hsa-miR- hsa-miR- hsa-miR-hsa-miR- 1285-5p 3157-3p 148b-5p 151a-3p 144-5p sig #14 0.021 1.105−0.004 0.572 1.335 hsa-miR- hsa-miR- hsa-miR- hsa-miR- hsa-let- 5001-3p1285-5p 5010-3p 151a-3p 7f-5p sig #15 0.021 1.098 −0.021 0.492 0.767brain-mir- hsa-miR- hsa-miR- hsa-miR- hsa-miR- 431 1285-5p 3157-3p151a-3p 144-5p sig #16 0.021 1.056 −0.038 0.579 1.180 hsa-miR- hsa-miR-brain-mir- hsa-miR- hsa-miR- 3127-3p 1285-5p 112 425-5p 151a-3p sig #170.022 1.164 0.003 0.641 1.335 hsa-miR- hsa-miR- hsa-miR- hsa-miR-hsa-let- 1285-5p 5010-3p 151a-3p 144-5p 7f-5p sig #18 0.022 1.140 −0.0150.691 0.649 hsa-miR- hsa-miR- brain-mir- hsa-miR- hsa-let- 550a-5p1285-5p 112 151a-3p 7f-5p sig #19 0.022 1.140 −0.014 0.441 1.033hsa-miR- brain-mir- hsa-miR- hsa-miR- hsa-miR- 1285-5p 112 148b-5p151a-3p 144-5p sig #20 0.022 1.137 −0.016 0.576 1.333 hsa-miR-brain-mir- hsa-miR- hsa-miR- hsa-let- 5001-3p 112 5010-3p 151a-3p 7f-5p

SUPPLEMENTAL TABLE 1 a Significantly down-regulated miRNAs in AD vs.controls. SEQ ID t-test p-value NO miRNA median AD median Control singleAUC 1 hsa-miR-144-5p 179.3082706767 913.7744360902 8.76E−08 0.09280303032 hsa-let-7f-5p 8334.1804511278 12867.954887218 7.60E−07 0.0710227273 3hsa-let-7e-5p 4971.7669172932 8212.9360902256 8.58E−07 0.1382575758 4hsa-let-7a-5p 8868.4511278196 15370.052631579 4.43E−06 0.0880681818 5hsa-miR-107 2433.0413533835 4822.3984962406 1.82E−05 0.203125 6hsa-let-7g-5p 1352.3684210526 3403.3759398496 2.69E−05 0.1259469697 7hsa-miR-103a- 2810.8458646617 5290.1278195489 3.89E−05 0.2088068182 3p 8hsa-miR-98 106.0864661654 217.3533834586 4.28E−05 0.1515151515 9hsa-miR-29c-3p 40.8327067669 74.8402255639 6.87E−05 0.1922348485 10hsa-miR-101-3p 56.1090225564 417.7105263158 0.0001143344 0.1463068182 11hsa-miR-548h- 3.6296992481 10.0845864662 0.000139166 0.1770833333 5p 12hsa-miR-106b- 1685.5864661654 2614.7518796993 0.0001453506 0.20975378793p 13 hsa-miR-15a-5p 598.484962406 1472.9962406015 0.00015541690.1557765152 14 hsa-miR-548g- 3.0338345865 9.6296992481 0.00021589170.1979166667 5p 15 hsa-miR-548ar- 3.0338345865 9.6296992481 0.00021589170.1979166667 5p 16 hsa-miR-548x- 3.0338345865 9.6296992481 0.00021589170.1979166667 5p 17 hsa-miR-548aj- 3.0338345865 9.6296992481 0.00021589170.1979166667 5p 18 hsa-let-7c 6147.5714285714 11249.5225563910.0002796738 0.1661931818 19 brain-mir-394 2.8026315789 7.83646616540.0003164566 0.2059659091 20 hsa-miR-1294 8.765037594 25.93796992480.0003239282 0.2026515152 21 brain-mir-170 2.8026315789 7.83646616540.0003241053 0.2069128788 22 hsa-miR-199a- 4.0883458647 12.4548872180.0003438893 0.1595643939 3p 23 brain-mir-149 2.8157894737 7.83646616540.000344696 0.2097537879 24 brain-mir-151 2.8157894737 7.83646616540.000344696 0.2097537879 25 brain-mir-370 178.4586466165 778.28947368420.0003625216 0.1553030303 26 hsa-miR-199b- 4.0883458647 12.62593984960.0003732986 0.1652462121 3p 27 brain-mir-333 2.8026315789 7.83646616540.0004122695 0.2069128788 28 hsa-miR-628-3p 2.954887218 7.11654135340.0004263003 0.2320075758 29 hsa-miR-190a 1.0488721805 5.59774436090.0004511324 0.1837121212 30 hsa-miR-29b-3p 11.8120300752 23.65037593980.0005076275 0.228219697 31 hsa-miR-660-5p 20.2537593985 72.91165413530.0006111848 0.1766098485 32 hsa-miR-143-3p 81.0676691729 168.07142857140.0006300042 0.2277462121 33 hsa-miR-548av- 3.2593984962 9.65413533830.0006819514 0.2196969697 5p 34 hsa-miR-548k 3.2593984962 9.65413533830.0006819514 0.2196969697 35 hsa-miR-29a-3p 43.6917293233 74.49436090230.0008592211 0.2414772727 36 hsa-miR-548i 0.1992481203 1.10902255640.0009595931 0.1482007576 37 hsa-miR-17-3p 32.5563909774 80.77819548870.0015924698 0.1410984848 38 brain-mir-398 10.0338345865 29.55263157890.0016819805 0.1964962121 39 hsa-miR-148a- 274.1748120301 845.52631578950.001762298 0.1586174242 3p 40 hsa-miR-126-3p 39.045112782108.8195488722 0.0028031688 0.2135416667 41 brain-mir-150 6.426691729319.4812030075 0.0034501841 0.2201704545 42 hsa-let-7i-5p 2907.42105263166027.2030075188 0.0034616244 0.2059659091 43 hsa-miR-33b-5p 0.2274436092.1240601504 0.0035364268 0.2580492424 44 hsa-miR-3200- 16.76503759423.5037593985 0.0045456431 0.3233901515 3p 45 hsa-miR-548o- 0.38345864661.7593984962 0.0047156877 0.2831439394 5p 46 hsa-miR-152 11.214285714322.2312030075 0.0052379113 0.1983901515 47 hsa-miR-548am- 0.48872180451.7593984962 0.0053080221 0.2878787879 5p 48 hsa-miR-548au- 0.48872180451.7593984962 0.0053080221 0.2878787879 5p 49 hsa-miR-548c- 0.48872180451.7593984962 0.0053080221 0.2878787879 5p 50 brain-mir-248S 0.24436090230.9285714286 0.0065438684 0.2547348485 51 hsa-miR-215 2042.39097744362997.969924812 0.008661199 0.3072916667 52 hsa-miR-340-5p 7.597744360921.984962406 0.0088183152 0.271780303 53 hsa-miR-1301 6.73308270689.5488721805 0.0089721175 0.2845643939 54 brain-mir-145 13.951127819517.7556390977 0.008979579 0.3143939394 55 hsa-miR-504 0.38345864661.8026315789 0.0093874443 0.3697916667

SUPPLEMENTAL TABLE 1 b Significantly up-regulated miRNAs in AD vs.controls. SEQ ID t-test p-value NO miRNA median AD median Control singleAUC 56 hsa-miR-30d-5p 11759.6691729323 7038.4962406015 9.25E−120.8863636364 57 hsa-miR-4781- 20.1597744361 10.0714285714 8.76E−100.8726325758 3p 58 hsa-miR-151a- 3303.037593985 1892.6616541353 3.49E−080.8645833333 3p 59 brain-mir-112 10.242481203 3.2687969925 4.77E−080.8735795455 60 hsa-miR-28-3p 1009.6466165414 537.7894736842 1.17E−070.7845643939 61 hsa-miR-26b-3p 73.6240601504 29.2105263158 1.18E−070.8333333333 62 hsa-miR-1468 80.1296992481 34.6466165414 9.00E−070.7732007576 63 hsa-miR-128 1204.3533834587 761.5676691729 9.93E−070.8238636364 64 hsa-miR-550a- 61.6052631579 39.4135338346 1.93E−060.8143939394 5p 65 hsa-miR-5010- 134.5263157895 77.8684210526 2.52E−060.8191287879 3p 66 hsa-miR-148b- 24.1278195489 12.8928571429 2.85E−060.8096590909 5p 67 brain-mir-395 7.8759398496 4.3233082707 3.18E−060.7935606061 68 brain-mir-308 7.8759398496 4.3233082707 3.18E−060.7935606061 69 hsa-miR-1285- 7.0695488722 3.2030075188 3.47E−060.7954545455 5p 70 hsa-miR-5001- 14.8796992481 7.0714285714 4.41E−060.8077651515 3p 71 hsa-miR-3127- 5.8421052632 2.4718045113 5.13E−060.7883522727 3p 72 hsa-miR-3157- 7.3778195489 3.1616541353 7.70E−060.8181818182 3p 73 brain-mir-431 6.2462406015 2.9436090226 8.30E−060.7869318182 74 hsa-miR-550a- 53.4661654135 31.9680451128 8.51E−060.7987689394 3-5p 75 hsa-miR-361-5p 51.6973684211 28.5733082707 1.18E−050.7940340909 76 brain-mir-83 160.5808270677 95.3872180451 1.37E−050.7367424242 77 hsa-miR-589-5p 305.6390977444 227.015037594 1.54E−050.7698863636 78 hsa-miR-425-5p 5290.1278195489 2907.4210526316 1.61E−050.8020833333 79 hsa-miR-30a-5p 10739.3759398496 7557.4210526316 2.66E−050.7826704545 80 brain-mir-79 3.5206766917 1.3026315789 2.85E−050.7552083333 81 brain-mir-80 3.5206766917 1.3026315789 2.85E−050.7552083333 82 hsa-miR-330-5p 10.7312030075 6.3402255639 3.46E−050.7722537879 83 hsa-miR-186-5p 4206.2932330827 2433.0413533835 3.46E−050.775094697 84 brain-mir-390 5.4191729323 3.1428571429 3.85E−050.7618371212 85 hsa-let-7d-3p 391.4060150376 208.9398496241 3.95E−050.7069128788 86 hsa-miR-328 396.6992481203 204.6898496241 4.08E−050.7168560606 87 hsa-miR-30c-5p 3195.7781954887 1563.7631578947 4.79E−050.7769886364 88 brain-mir-200 30.3740601504 15.8233082707 5.41E−050.7665719697 89 hsa-miR-363-3p 6371.4285714286 4971.7669172932 5.51E−050.7552083333 90 hsa-miR-339-3p 125.3120300752 87.8345864662 5.67E−050.7471590909 91 brain-mir-114 1009.6466165414 543.5526315789 5.76E−050.6856060606 92 hsa-miR-942 512.7142857143 306.2894736842 6.12E−050.6851325758 93 hsa-miR-345-5p 470.6090225564 317.9210526316 6.17E−050.7481060606 94 brain-mir-247 2997.969924812 1634.6879699248 7.23E−050.7315340909 95 hsa-miR-4742- 43.2030075188 27.6635338346 7.99E−050.7201704545 3p 96 brain-mir-314 3614.8045112782 2124.57518796998.13E−05 0.7324810606 97 brain-mir-12 2433.0413533835 1370.53383458659.13E−05 0.7220643939 98 brain-mir-232 75.0733082707 39.92857142869.70E−05 0.6799242424 99 brain-mir-424S 4.8571428571 2.15037593980.0001134253 0.7608901515 100 brain-mir-219 28.5751879699 15.78195488720.0001441433 0.7736742424 101 hsa-miR-10a-5p 827.0977443609443.9586466165 0.0001696328 0.7334280303 102 hsa-miR-3605-280.9135338346 187.6466165414 0.0001817728 0.6837121212 3p 103brain-mir-52 9.2406015038 4.6503759398 0.0002065404 0.7817234848 104brain-mir-53 6.7462406015 3.8909774436 0.0002097674 0.7604166667 105hsa-miR-3157- 0.3721804511 0.1240601504 0.0002118311 0.7277462121 5p 106brain-mir-41S 10.5733082707 5.9191729323 0.0002570966 0.7803030303 107brain-mir-201 15.4248120301 9.5469924812 0.000293033 0.7291666667 108hsa-miR-5006- 2.5921052632 1.4210526316 0.0003127522 0.743844697 3p 109hsa-miR-4659a- 7.2255639098 4.0977443609 0.0003606508 0.7447916667 3p110 brain-mir-279 10.1334586466 5.1541353383 0.000437069 0.6955492424111 brain-mir-111 986.477443609 590.4022556391 0.0004713764 0.7504734848112 brain-mir-88 2.3646616541 1.3778195489 0.0005681084 0.6912878788 113brain-mir-251 1.8909774436 0.8458646617 0.0005688548 0.7296401515 114hsa-miR-4435 51.0902255639 33.9661654135 0.0005693209 0.7230113636 115hsa-miR-5690 11.3984962406 7.5281954887 0.0005745024 0.7253787879 116brain-mir-166 2.4210526316 1.0921052632 0.0006242931 0.7149621212 117brain-mir-193 1.6127819549 0.8402255639 0.0006339444 0.7002840909 118hsa-miR-625-5p 7.3590225564 4.3571428571 0.0006972852 0.7575757576 119hsa-miR-10b-5p 683.6766917293 406.3007518797 0.0008299916 0.7168560606120 brain-mir-299 3.9586466165 1.7857142857 0.000839426 0.7069128788 121brain-mir-153 0.5751879699 0.1428571429 0.0008478946 0.6860795455 122hsa-miR-758 0.5939849624 0.1240601504 0.0008889247 0.7589962121 123hsa-miR-30a-3p 114.6278195489 67.3947368421 0.0009850641 0.7357954545124 brain-mir-220 36.4530075188 24.4511278195 0.0010085027 0.7182765152125 brain-mir-392 5.5695488722 3.1447368421 0.001117105 0.6586174242 126brain-mir-102 34.0526315789 22.9229323308 0.0011430551 0.7571022727 127hsa-let-7b-3p 47.2894736842 26.0338345865 0.0011483131 0.7471590909 128hsa-miR-340-3p 23.6879699248 9.4248120301 0.0011789284 0.7651515152 129hsa-miR-484 21682.0451127819 14260.5789473684 0.0012569269 0.7211174242130 hsa-miR-30e-3p 169.3082706767 121.1917293233 0.00134405340.7381628788 131 brain-mir-72S 0.4436090226 0.1240601504 0.00142255720.7348484848 132 hsa-miR-371b- 4.7142857143 2.2706766917 0.00143892810.7258522727 5p 133 hsa-miR-5581- 2.3327067669 1.5620300752 0.00155463370.7064393939 3p 134 brain-mir-399 19.1616541353 12.77067669170.0015845513 0.6619318182 135 brain-mir-403 4.1842105263 2.83646616540.0016408632 0.6695075758 136 brain-mir-73 21.1766917293 12.9924812030.0016958209 0.6922348485 137 brain-mir-190 4.3233082707 2.35902255640.0020611484 0.6903409091 138 brain-mir-188 4.3233082707 2.35902255640.0020611484 0.6903409091 139 brain-mir-189 4.3233082707 2.35902255640.0020611484 0.6903409091 140 brain-mir-192 4.3233082707 2.35902255640.0020611484 0.6903409091 141 brain-mir-311 382.2819548872266.9248120301 0.0022861501 0.6373106061 142 brain-mir-161 17.488721804510.5 0.0024185375 0.7424242424 143 hsa-miR-3074- 24.01503759415.7105263158 0.002419588 0.740530303 5p 144 hsa-miR-30b-5p443.9586466165 292.2105263158 0.0024240637 0.712594697 145hsa-miR-576-5p 291.3834586466 207.484962406 0.0024324256 0.7215909091146 brain-mir-23 16.2218045113 11.3665413534 0.0024712736 0.71875 147hsa-miR-943 2.0789473684 1.3984962406 0.0025973005 0.6903409091 148brain-mir-351 0.272556391 0.1278195489 0.0026770024 0.6439393939 149hsa-miR-4772- 1.0601503759 0.219924812 0.0030588227 0.6884469697 3p 150brain-mir-319 4.954887218 3.6860902256 0.0031658495 0.6912878788 151hsa-miR-937 13.8984962406 8.4323308271 0.0032014572 0.6174242424 152hsa-miR-181a- 222.4135338346 173.3458646617 0.0034658731 0.67708333332-3p 153 hsa-miR-4755- 6.4661654135 4.0789473684 0.0035891030.6590909091 5p 154 hsa-miR-3909 7.7011278195 4.1691729323 0.00366343270.7466856061 155 hsa-miR-1260b 548 436.8947368421 0.0037982461 0.640625156 brain-mir-293 3.4022556391 2.0056390977 0.0043533661 0.6879734848157 brain-mir-160 13.1635338346 9.3646616541 0.0047314115 0.6496212121158 hsa-miR-2110 37.5056390977 20.3082706767 0.0048976896 0.7755681818159 hsa-miR-584-3p 1.6184210526 0.8289473684 0.0049666999 0.6401515152160 brain-mir-129 1.2312030075 0.8139097744 0.0052865283 0.6557765152161 hsa-miR-1280 2.8233082707 1.1860902256 0.0054091313 0.6519886364 162hsa-miR-3180- 1.0939849624 0.515037594 0.0064691451 0.6557765152 5p 163hsa-miR-668 0.3289473684 0.1390977444 0.0064710752 0.640625 164hsa-miR-4512 2.0112781955 0.787593985 0.0068965461 0.6638257576 165hsa-miR-641 10.0902255639 7.5620300752 0.0069660105 0.6619318182 166hsa-miR-1233 2.0601503759 0.9285714286 0.007463631 0.6586174242 167hsa-miR-378a- 10.0263157895 5.4755639098 0.0075454956 0.7149621212 5p168 hsa-miR-26a-5p 5634.0676691729 4206.2932330827 0.0078297310.6789772727 169 brain-mir-258 5.6973684211 0.8233082707 0.00790158910.7201704545 170 hsa-miR-1260a 553.045112782 456.4210526316 0.00913014920.6070075758

SUPPLEMENTAL TABLE 2 Newly discovered miRNA markers SEQ ID NO miRNASequence 126 brain-mir-102 UAUGGAGGUCUCUGUCUGGCU 111 brain-mir-111CACUGCUAAAUUUGGCUGGCUU  59 brain-mir-112 AGCUCUGUCUGUGUCUCUAGG  91brain-mir-114 CACUGCAACCUCUGCCUCCGGU  97 brain-mir-12ACUCCCACUGCUUGACUUGACUAG 160 brain-mir-129 CAUGGUCCAUUUUGCUCUGCU  54brain-mir-145 AAGCACUGCCUUUGAACCUGA  23 brain-mir-149AAAAGUAAUCGCACUUUUUG  41 brain-mir-150 UGAGGUAGUAGGUGGUGUGC  24brain-mir-151 AAAAGUAAUCGCACUUUUUG 121 brain-mir-153CCUCUUCUCAGAACACUUCCUGG 157 brain-mir-160 CACUGCAACCUCUGCCUCC 142brain-mir-161 CUUCGAAAGCGGCUUCGGCU 116 brain-mir-166CUGGCUGCUUCCCUUGGUCU  21 brain-mir-170 AAAAGUAAUGGCAGUUUUUG 138brain-mir-188 CCUGACCCCCAUGUCGCCUCUGU 139 brain-mir-189CCUGACCCCCAUGUCGCCUCUGU 137 brain-mir-190 CCUGACCCCCAUGUCGCCUCUGU 140brain-mir-192 CCUGACCCCCAUGUCGCCUCUGU 117 brain-mir-193AUCCCUUUAUCUGUCCUCUAGG  88 brain-mir-200 UUCCUGGCUCUCUGUUGCACA 107brain-mir-201 CACCCCACCAGUGCAGGCUG 100 brain-mir-219UCAAGUGUCAUCUGUCCCUAGG 124 brain-mir-220 UCCGGAUCCGGCUCCGCGCCU 146brain-mir-23 UUAGUGGCUCCCUCUGCCUGCA  98 brain-mir-232UUGCUCUGCUCUCCCUUGUACU  94 brain-mir-247 ACGCCCACUGCUUCACUUGACUAG  50brain-mir-248S GGCGGCGGAGGCGGCGGUG 113 brain-mir-251 UGGCCCAAGACCUCAGACC169 brain-mir-258 AUCCCACCCCUGCCCCCA 110 brain-mir-279AUCCCACCGCUGCCACAC 156 brain-mir-293 UUGGUGAGGACCCCAAGCUCGG 120brain-mir-299 CAUGCCACUGCACUCCAGCCU  68 brain-mir-308CACUGCACUCCAGCCUGGGUGA 141 brain-mir-311 CACUGCAACCUCUGCCUCCCGA  96brain-mir-314 ACUCCCACUGCUUCACUUGAUUAG 150 brain-mir-319CUGCACUCCAGCCUGGGCGA  27 brain-mir-333 AAAAGUAAUCGCAGGUUUUG 148brain-mir-351 UGUCUUGCUCUGUUGCCCAGGU  25 brain-mir-370GGCUGGUCUGAUGGUAGUGGGUUA  84 brain-mir-390 ACUGCAACCUCCACCUCCUGGGU 125brain-mir-392 CCCGCCUGUCUCUCUCUUGCA  19 brain-mir-394AAAAGUAAUCGUAGUUUUUG  67 brain-mir-395 CACUGCACUCCAGCCUGGGUGA  38brain-mir-398 GGCUGGUCCGAGUGCAGUGGUGUU 134 brain-mir-399CACUGCAACCUCUGCCUCC 135 brain-mir-403 AAAGACUUCCUUCUCUCGCCU 106brain-mir-41S CCCCGCGCAGGUUCGAAUCCUG  99 brain-mir-424SCACUGCACUCCAGCCUGGGUA  73 brain-mir-431 CUCGGCCUUUGCUCGCAGCACU 103brain-mir-52 CUGCACUCCAGCCUGGGCGAC 104 brain-mir-53CCCAGGACAGUUUCAGUGAUG 131 brain-mir-72S GACCACACUCCAUCCUGGGC 136brain-mir-73 UCCGGAUGUGCUGACCCCUGCG  80 brain-mir-79CACUGCACUCCAGCCUGGCU  81 brain-mir-80 CACUGCACUCCAGCCUGGCU  76brain-mir-83 CAGGGUCUCGUUCUGUUGCC 112 brain-mir-88UCUUCACCUGCCUCUGCCUGCA

SUPPLEMENTAL TABLE 3 Significantly up- or down-regulated miRNAs in MCIvs. controls. SEQ ID median t-test p-value NO Marker median MCI Controlsingle AUC 171 hsa-miR-29c-3p 31.34210526 74.84022556 1.39E−070.061363636 172 hsa-miR-29a-3p 39.20676692 74.4943609 2.00E−060.093181818 173 hsa-let-7e-5p 5465.075188 8212.93609 5.97E−060.139772727 174 hsa-let-7a-5p 9288.364662 15370.05263 1.19E−050.110227273 175 hsa-let-7f-5p 8601.315789 12867.95489 1.48E−05 0.1125176 hsa-miR-29b-3p 9.746240602 23.65037594 9.48E−05 0.160227273 177hsa-miR-98 98.17293233 217.3533835 0.00019379 0.152272727 178hsa-miR-425-5p 5634.067669 2907.421053 0.000351963 0.818181818 179hsa-miR-223-3p 328.8571429 470.6090226 0.000468269 0.230681818 180hsa-miR-181a-2- 241.5451128 173.3458647 0.000505662 0.805681818 3p 181hsa-miR-148b-3p 137.6541353 279.3120301 0.000811319 0.194318182 182brain-mir-145 9.477443609 17.7556391 0.000969848 0.209090909 183hsa-miR-548h-5p 4.864661654 10.08458647 0.000996949 0.198863636 184hsa-miR-550a-5p 64.54323308 39.41353383 0.001127581 0.807954545 185hsa-miR-374b-5p 10.30639098 20.54511278 0.001150103 0.222727273 186hsa-miR-339-3p 126.4360902 87.83458647 0.00120356 0.811363636 187hsa-miR-3661 1.357142857 3.716165414 0.001208331 0.210227273 188brain-mir-190 6.342105263 2.359022556 0.001522223 0.818181818 189brain-mir-188 6.342105263 2.359022556 0.001522223 0.818181818 190brain-mir-189 6.342105263 2.359022556 0.001522223 0.818181818 191brain-mir-192 6.342105263 2.359022556 0.001522223 0.818181818 192hsa-miR-550a-3- 54.72368421 31.96804511 0.001581747 0.759090909 5p 193hsa-miR-199a-3p 4.171052632 12.45488722 0.001641108 0.204545455 194hsa-miR-199b-3p 4.221804511 12.62593985 0.001650922 0.205681818 195hsa-miR-660-5p 35.97744361 72.91165414 0.001678456 0.221590909 196hsa-miR-190a 1.609022556 5.597744361 0.001784374 0.204545455 197brain-mir-220 48.59022556 24.45112782 0.002184462 0.790909091 198hsa-miR-548g-5p 3.447368421 9.629699248 0.002357652 0.225 199hsa-miR-548ar- 3.447368421 9.629699248 0.002357652 0.225 5p 200hsa-miR-548x-5p 3.447368421 9.629699248 0.002357652 0.225 201hsa-miR-548aj- 3.447368421 9.629699248 0.002357652 0.225 5p 202brain-mir-394 2.603383459 7.836466165 0.002559946 0.215909091 203brain-mir-149 2.603383459 7.836466165 0.002559946 0.215909091 204brain-mir-151 2.603383459 7.836466165 0.002559946 0.215909091 205hsa-let-7c 6816.890977 11249.52256 0.002574232 0.196590909 206brain-mir-333 2.603383459 7.836466165 0.002690942 0.215909091 207brain-mir-170 2.603383459 7.836466165 0.002759117 0.225 208 hsa-miR-15212.7443609 22.23120301 0.00331602 0.222727273 209 hsa-miR-15a-5p632.3984962 1472.996241 0.003376847 0.2 210 hsa-miR-197-5p 0.8308270680.135338346 0.00340422 0.811363636 211 brain-mir-399 21.751879712.77067669 0.003703683 0.781818182 212 hsa-miR-3158-3p 433.6691729309.3571429 0.003815704 0.732954545 213 brain-mir-150 12.1541353419.48120301 0.003816641 0.284090909 214 hsa-miR-424-3p 194.537594105.6146617 0.003852425 0.775 215 hsa-miR-148a-3p 578.1203008845.5263158 0.004120012 0.240909091 216 hsa-miR-3200-3p 16.6447368423.5037594 0.004405877 0.303409091 217 hsa-miR-628-3p 2.7969924817.116541353 0.004410063 0.243181818 218 hsa-let-7d-5p 412.6240602598.4849624 0.004602573 0.217045455 219 hsa-miR-4781-3p 13.9661654110.07142857 0.004719502 0.769318182 220 brain-mir-160 17.842105269.364661654 0.005169293 0.768181818 221 hsa-miR-374a-5p 1.7932330835.186090226 0.005650498 0.276136364 222 hsa-miR-338-3p 0.5939849622.716165414 0.006017454 0.302272727 223 hsa-miR-340-5p 8.18796992521.98496241 0.006522277 0.252272727 224 brain-mir-395 5.8909774444.323308271 0.006577993 0.719318182 225 brain-mir-308 5.8909774444.323308271 0.006577993 0.719318182 226 brain-mir-53 5.7575187973.890977444 0.006988766 0.7125 227 brain-mir-229 0.417293233 1.8646616540.007037494 0.192045455 228 hsa-miR-151a-3p 3088.518797 1892.6616540.00727488 0.713636364 229 hsa-miR-1234 2.323308271 5.624060150.00831879 0.270454545 230 hsa-miR-874 6.437969925 10.026315790.008872069 0.269318182 231 hsa-miR-548av- 3.906015038 9.6541353380.008945083 0.245454545 5p 232 hsa-miR-548k 3.906015038 9.6541353380.008945083 0.245454545 233 brain-mir-101 3.883458647 6.6334586470.009086578 0.271590909 234 hsa-miR-30d-5p 10223.82707 7038.4962410.009299073 0.729545455 235 hsa-miR-3200-5p 22 37.82706767 0.009548280.282954545

SUPPLEMENTAL TABLE 4 Overview of miRNA markers, includingsequence information   1 hsa-miR-144-5p GGAUAUCAUCAUAUACUGUAAG   2hsa-let-7f-5p UGAGGUAGUAGAUUGUAUAGUU   3 hsa-let-7e-5pUGAGGUAGGAGGUUGUAUAGUU   4 hsa-let-7a-5p UGAGGUAGUAGGUUGUAUAGUU   5hsa-miR-107 AGCAGCAUUGUACAGGGCUAUCA   6 hsa-let-7g-5pUGAGGUAGUAGUUUGUACAGUU   7 hsa-miR-103a-3p AGCAGCAUUGUACAGGGCUAUGA   8hsa-miR-98 UGAGGUAGUAAGUUGUAUUGUU   9 hsa-miR-29c-3pUAGCACCAUUUGAAAUCGGUUA  10 hsa-miR-101-3p UACAGUACUGUGAUAACUGAA  11hsa-miR-548h-5p AAAAGUAAUCGCGGUUUUUGUC  12 hsa-miR-106b-3pCCGCACUGUGGGUACUUGCUGC  13 hsa-miR-15a-5p UAGCAGCACAUAAUGGUUUGUG  14hsa-miR-548g-5p UGCAAAAGUAAUUGCAGUUUUUG  15 hsa-miR-548ar-5pAAAAGUAAUUGCAGUUUUUGC  16 hsa-miR-548x-5p UGCAAAAGUAAUUGCAGUUUUUG  17hsa-miR-548aj-5p UGCAAAAGUAAUUGCAGUUUUUG  18 hsa-let-7cUGAGGUAGUAGGUUGUAUGGUU  19 brain-mir-394 AAAAGUAAUCGUAGUUUUUG  20hsa-miR-1294 UGUGAGGUUGGCAUUGUUGUCU  21 brain-mir-170AAAAGUAAUGGCAGUUUUUG  22 hsa-miR-199a-3p ACAGUAGUCUGCACAUUGGUUA  23brain-mir-149 AAAAGUAAUCGCACUUUUUG  24 brain-mir-151AAAAGUAAUCGCACUUUUUG  25 brain-mir-370 GGCUGGUCUGAUGGUAGUGGGUUA  26hsa-miR-199b-3p ACAGUAGUCUGCACAUUGGUUA  27 brain-mir-333AAAAGUAAUCGCAGGUUUUG  28 hsa-miR-628-3p UCUAGUAAGAGUGGCAGUCGA  29hsa-miR-190a UGAUAUGUUUGAUAUAUUAGGU  30 hsa-miR-29b-3pUAGCACCAUUUGAAAUCAGUGUU  31 hsa-miR-660-5p UACCCAUUGCAUAUCGGAGUUG  32hsa-miR-143-3p UGAGAUGAAGCACUGUAGCUC  33 hsa-miR-548av-5pAAAAGUACUUGCGGAUUU  34 hsa-miR-548k AAAAGUACUUGCGGAUUUUGCU  35hsa-miR-29a-3p UAGCACCAUCUGAAAUCGGUUA  36 hsa-miR-548iAAAAGUAAUUGCGGAUUUUGCC  37 hsa-miR-17-3p ACUGCAGUGAAGGCACUUGUAG  38brain-mir-398 GGCUGGUCCGAGUGCAGUGGUGUU  39 hsa-miR-148a-3pUCAGUGCACUACAGAACUUUGU  40 hsa-miR-126-3p UCGUACCGUGAGUAAUAAUGCG  41brain-mir-150 UGAGGUAGUAGGUGGUGUGC  42 hsa-let-7i-5pUGAGGUAGUAGUUUGUGCUGUU  43 hsa-miR-33b-5p GUGCAUUGCUGUUGCAUUGC  44hsa-miR-3200-3p CACCUUGCGCUACUCAGGUCUG  45 hsa-miR-548o-5pAAAAGUAAUUGCGGUUUUUGCC  46 hsa-miR-152 UCAGUGCAUGACAGAACUUGG  47hsa-miR-548am-5p AAAAGUAAUUGCGGUUUUUGCC  48 hsa-miR-548au-5pAAAAGUAAUUGCGGUUUUUGC  49 hsa-miR-548c-5p AAAAGUAAUUGCGGUUUUUGCC  50brain-mir-248S GGCGGCGGAGGCGGCGGUG  51 hsa-miR-215 AUGACCUAUGAAUUGACAGAC 52 hsa-miR-340-5p UUAUAAAGCAAUGAGACUGAUU  53 hsa-miR-1301UUGCAGCUGCCUGGGAGUGACUUC  54 brain-mir-145 AAGCACUGCCUUUGAACCUGA  55hsa-miR-504 AGACCCUGGUCUGCACUCUAUC  56 hsa-miR-30d-5pUGUAAACAUCCCCGACUGGAAG  57 hsa-miR-4781-3p AAUGUUGGAAUCCUCGCUAGAG  58hsa-miR-151a-3p CUAGACUGAAGCUCCUUGAGG  59 brain-mir-112AGCUCUGUCUGUGUCUCUAGG  60 hsa-miR-28-3p CACUAGAUUGUGAGCUCCUGGA  61hsa-miR-26b-3p CCUGUUCUCCAUUACUUGGCUC  62 hsa-miR-1468CUCCGUUUGCCUGUUUCGCUG  63 hsa-miR-128 UCACAGUGAACCGGUCUCUUU  64hsa-miR-550a-5p AGUGCCUGAGGGAGUAAGAGCCC  65 hsa-miR-5010-3pUUUUGUGUCUCCCAUUCCCCAG  66 hsa-miR-148b-5p AAGUUCUGUUAUACACUCAGGC  67brain-mir-395 CACUGCACUCCAGCCUGGGUGA  68 brain-mir-308CACUGCACUCCAGCCUGGGUGA  69 hsa-miR-1285-5p GAUCUCACUUUGUUGCCCAGG  70hsa-miR-5001-3p UUCUGCCUCUGUCCAGGUCCUU  71 hsa-miR-3127-3pUCCCCUUCUGCAGGCCUGCUGG  72 hsa-miR-3157-3p CUGCCCUAGUCUAGCUGAAGCU  73brain-mir-431 CUCGGCCUUUGCUCGCAGCACU  74 hsa-miR-550a-3-5pAGUGCCUGAGGGAGUAAGAG  75 hsa-miR-361-5p UUAUCAGAAUCUCCAGGGGUAC  76brain-mir-83 CAGGGUCUCGUUCUGUUGCC  77 hsa-miR-589-5pUGAGAACCACGUCUGCUCUGAG  78 hsa-miR-425-5p AAUGACACGAUCACUCCCGUUGA  79hsa-miR-30a-5p UGUAAACAUCCUCGACUGGAAG  80 brain-mir-79CACUGCACUCCAGCCUGGCU  81 brain-mir-80 CACUGCACUCCAGCCUGGCU  82hsa-miR-330-5p UCUCUGGGCCUGUGUCUUAGGC  83 hsa-miR-186-5pCAAAGAAUUCUCCUUUUGGGCU  84 brain-mir-390 ACUGCAACCUCCACCUCCUGGGU  85hsa-let-7d-3p CUAUACGACCUGCUGCCUUUCU  86 hsa-miR-328CUGGCCCUCUCUGCCCUUCCGU  87 hsa-miR-30c-5p UGUAAACAUCCUACACUCUCAGC  88brain-mir-200 UUCCUGGCUCUCUGUUGCACA  89 hsa-miR-363-3pAAUUGCACGGUAUCCAUCUGUA  90 hsa-miR-339-3p UGAGCGCCUCGACGACAGAGCCG  91brain-mir-114 CACUGCAACCUCUGCCUCCGGU  92 hsa-miR-942UCUUCUCUGUUUUGGCCAUGUG  93 hsa-miR-345-5p GCUGACUCCUAGUCCAGGGCUC  94brain-mir-247 ACGCCCACUGCUUCACUUGACUAG  95 hsa-miR-4742-3pUCUGUAUUCUCCUUUGCCUGCAG  96 brain-mir-314 ACUCCCACUGCUUCACUUGAUUAG  97brain-mir-12 ACUCCCACUGCUUGACUUGACUAG  98 brain-mir-232UUGCUCUGCUCUCCCUUGUACU  99 brain-mir-424S CACUGCACUCCAGCCUGGGUA 100brain-mir-219 UCAAGUGUCAUCUGUCCCUAGG 101 hsa-miR-10a-5pUACCCUGUAGAUCCGAAUUUGUG 102 hsa-miR-3605-3p CCUCCGUGUUACCUGUCCUCUAG 103brain-mir-52 CUGCACUCCAGCCUGGGCGAC 104 brain-mir-53CCCAGGACAGUUUCAGUGAUG 105 hsa-miR-3157-5p UUCAGCCAGGCUAGUGCAGUCU 106brain-mir-41S CCCCGCGCAGGUUCGAAUCCUG 107 brain-mir-201CACCCCACCAGUGCAGGCUG 108 hsa-miR-5006-3p UUUCCCUUUCCAUCCUGGCAG 109hsa-miR-4659a-3p UUUCUUCUUAGACAUGGCAACG 110 brain-mir-279AUCCCACCGCUGCCACAC 111 brain-mir-111 CACUGCUAAAUUUGGCUGGCUU 112brain-mir-88 UCUUCACCUGCCUCUGCCUGCA 113 brain-mir-251UGGCCCAAGACCUCAGACC 114 hsa-miR-4435 AUGGCCAGAGCUCACACAGAGG 115hsa-miR-5690 UCAGCUACUACCUCUAUUAGG 116 brain-mir-166CUGGCUGCUUCCCUUGGUCU 117 brain-mir-193 AUCCCUUUAUCUGUCCUCUAGG 118hsa-miR-625-5p AGGGGGAAAGUUCUAUAGUCC 119 hsa-miR-10b-5pUACCCUGUAGAACCGAAUUUGUG 120 brain-mir-299 CAUGCCACUGCACUCCAGCCU 121brain-mir-153 CCUCUUCUCAGAACACUUCCUGG 122 hsa-miR-758UUUGUGACCUGGUCCACUAACC 123 hsa-miR-30a-3p CUUUCAGUCGGAUGUUUGCAGC 124brain-mir-220 UCCGGAUCCGGCUCCGCGCCU 125 brain-mir-392CCCGCCUGUCUCUCUCUUGCA 126 brain-mir-102 UAUGGAGGUCUCUGUCUGGCU 127hsa-let-7b-3p CUAUACAACCUACUGCCUUCCC 128 hsa-miR-340-3pUCCGUCUCAGUUACUUUAUAGC 129 hsa-miR-484 UCAGGCUCAGUCCCCUCCCGAU 130hsa-miR-30e-3p CUUUCAGUCGGAUGUUUACAGC 131 brain-mir-72SGACCACACUCCAUCCUGGGC 132 hsa-miR-371b-5p ACUCAAAAGAUGGCGGCACUUU 133hsa-miR-5581-3p UUCCAUGCCUCCUAGAAGUUCC 134 brain-mir-399CACUGCAACCUCUGCCUCC 135 brain-mir-403 AAAGACUUCCUUCUCUCGCCU 136brain-mir-73 UCCGGAUGUGCUGACCCCUGCG 137 brain-mir-190CCUGACCCCCAUGUCGCCUCUGU 138 brain-mir-188 CCUGACCCCCAUGUCGCCUCUGU 139brain-mir-189 CCUGACCCCCAUGUCGCCUCUGU 140 brain-mir-192CCUGACCCCCAUGUCGCCUCUGU 141 brain-mir-311 CACUGCAACCUCUGCCUCCCGA 142brain-mir-161 CUUCGAAAGCGGCUUCGGCU 143 hsa-miR-3074-5pGUUCCUGCUGAACUGAGCCAG 144 hsa-miR-30b-5p UGUAAACAUCCUACACUCAGCU 145hsa-miR-576-5p AUUCUAAUUUCUCCACGUCUUU 146 brain-mir-23UUAGUGGCUCCCUCUGCCUGCA 147 hsa-miR-943 CUGACUGUUGCCGUCCUCCAG 148brain-mir-351 UGUCUUGCUCUGUUGCCCAGGU 149 hsa-miR-4772-3pCCUGCAACUUUGCCUGAUCAGA 150 brain-mir-319 CUGCACUCCAGCCUGGGCGA 151hsa-miR-937 AUCCGCGCUCUGACUCUCUGCC 152 hsa-miR-181a-2-3pACCACUGACCGUUGACUGUACC 153 hsa-miR-4755-5p UUUCCCUUCAGAGCCUGGCUUU 154hsa-miR-3909 UGUCCUCUAGGGCCUGCAGUCU 155 hsa-miR-1260bAUCCCACCACUGCCACCAU 156 brain-mir-293 UUGGUGAGGACCCCAAGCUCGG 157brain-mir-160 CACUGCAACCUCUGCCUCC 158 hsa-miR-2110UUGGGGAAACGGCCGCUGAGUG 159 hsa-miR-584-3p UCAGUUCCAGGCCAACCAGGCU 160brain-mir-129 CAUGGUCCAUUUUGCUCUGCU 161 hsa-miR-1280 UCCCACCGCUGCCACCC162 hsa-miR-3180-5p CUUCCAGACGCUCCGCCCCACGUCG 163 hsa-miR-668UGUCACUCGGCUCGGCCCACUAC 164 hsa-miR-4512 CAGGGCCUCACUGUAUCGCCCA 165hsa-miR-641 AAAGACAUAGGAUAGAGUCACCUC 166 hsa-miR-1233UGAGCCCUGUCCUCCCGCAG 167 hsa-miR-378a-5p CUCCUGACUCCAGGUCCUGUGU 168hsa-miR-26a-5p UUCAAGUAAUCCAGGAUAGGCU 169 brain-mir-258AUCCCACCCCUGCCCCCA 170 hsa-miR-1260a AUCCCACCUCUGCCACCA 171hsa-miR-29c-3p UAGCACCAUUUGAAAUCGGUUA 172 hsa-miR-29a-3pUAGCACCAUCUGAAAUCGGUUA 173 hsa-let-7e-5p UGAGGUAGGAGGUUGUAUAGUU 174hsa-let-7a-5p UGAGGUAGUAGGUUGUAUAGUU 175 hsa-let-7f-5pUGAGGUAGUAGAUUGUAUAGUU 176 hsa-miR-29b-3p UAGCACCAUUUGAAAUCAGUGUU 177hsa-miR-98 UGAGGUAGUAAGUUGUAUUGUU 178 hsa-miR-425-5pAAUGACACGAUCACUCCCGUUGA 179 hsa-miR-223-3p UGUCAGUUUGUCAAAUACCCCA 180hsa-miR-181a-2-3p ACCACUGACCGUUGACUGUACC 181 hsa-miR-148b-3pUCAGUGCAUCACAGAACUUUGU 182 brain-mir-145 AAGCACUGCCUUUGAACCUGA 183hsa-miR-548h-5p AAAAGUAAUCGCGGUUUUUGUC 184 hsa-miR-550a-5pAGUGCCUGAGGGAGUAAGAGCCC 185 hsa-miR-374b-5p AUAUAAUACAACCUGCUAAGUG 186hsa-miR-339-3p UGAGCGCCUCGACGACAGAGCCG 187 hsa-miR-3661UGACCUGGGACUCGGACAGCUG 188 brain-mir-190 CCUGACCCCCAUGUCGCCUCUGU 189brain-mir-188 CCUGACCCCCAUGUCGCCUCUGU 190 brain-mir-189CCUGACCCCCAUGUCGCCUCUGU 191 brain-mir-192 CCUGACCCCCAUGUCGCCUCUGU 192hsa-miR-550a-3-5p AGUGCCUGAGGGAGUAAGAG 193 hsa-miR-199a-3pACAGUAGUCUGCACAUUGGUUA 194 hsa-miR-199b-3p ACAGUAGUCUGCACAUUGGUUA 195hsa-miR-660-5p UACCCAUUGCAUAUCGGAGUUG 196 hsa-miR-190aUGAUAUGUUUGAUAUAUUAGGU 197 brain-mir-220 UCCGGAUCCGGCUCCGCGCCU 198hsa-miR-548g-5p UGCAAAAGUAAUUGCAGUUUUUG 199 hsa-miR-548ar-5pAAAAGUAAUUGCAGUUUUUGC 200 hsa-miR-548x-5p UGCAAAAGUAAUUGCAGUUUUUG 201hsa-miR-548aj-5p UGCAAAAGUAAUUGCAGUUUUUG 202 brain-mir-394AAAAGUAAUCGUAGUUUUUG 203 brain-mir-149 AAAAGUAAUCGCACUUUUUG 204brain-mir-151 AAAAGUAAUCGCACUUUUUG 205 hsa-let-7c UGAGGUAGUAGGUUGUAUGGUU206 brain-mir-333 AAAAGUAAUCGCAGGUUUUG 207 brain-mir-170AAAAGUAAUGGCAGUUUUUG 208 hsa-miR-152 UCAGUGCAUGACAGAACUUGG 209hsa-miR-15a-5p UAGCAGCACAUAAUGGUUUGUG 210 hsa-miR-197-5pCGGGUAGAGAGGGCAGUGGGAGG 211 brain-mir-399 CACUGCAACCUCUGCCUCC 212hsa-miR-3158-3p AAGGGCUUCCUCUCUGCAGGAC 213 brain-mir-150UGAGGUAGUAGGUGGUGUGC 214 hsa-miR-424-3p CAAAACGUGAGGCGCUGCUAU 215hsa-miR-148a-3p UCAGUGCACUACAGAACUUUGU 216 hsa-miR-3200-3pCACCUUGCGCUACUCAGGUCUG 217 hsa-miR-628-3p UCUAGUAAGAGUGGCAGUCGA 218hsa-let-7d-5p AGAGGUAGUAGGUUGCAUAGUU 219 hsa-miR-4781-3pAAUGUUGGAAUCCUCGCUAGAG 220 brain-mir-160 CACUGCAACCUCUGCCUCC 221hsa-miR-374a-5p UUAUAAUACAACCUGAUAAGUG 222 hsa-miR-338-3pUCCAGCAUCAGUGAUUUUGUUG 223 hsa-miR-340-5p UUAUAAAGCAAUGAGACUGAUU 224brain-mir-395 CACUGCACUCCAGCCUGGGUGA 225 brain-mir-308CACUGCACUCCAGCCUGGGUGA 226 brain-mir-53 CCCAGGACAGUUUCAGUGAUG 227brain-mir-229 AUCCCACCUCUGCUACCA 228 hsa-miR-151a-3pCUAGACUGAAGCUCCUUGAGG 229 hsa-miR-1234 UCGGCCUGACCACCCACCCCAC 230hsa-miR-874 CUGCCCUGGCCCGAGGGACCGA 231 hsa-miR-548av-5pAAAAGUACUUGCGGAUUU 232 hsa-miR-548k AAAAGUACUUGCGGAUUUUGCU 233brain-mir-101 AGACCUACUUAUCUACCAACA 234 hsa-miR-30d-5pUGUAAACAUCCCCGACUGGAAG 235 hsa-miR-3200-5p AAUCUGAGAAGGCGCACAAGGU

1. A method of classifying a sample of a patient suffering from or atrisk of developing Alzheimer's Disease, wherein said sample is a bloodsample, said method comprising the steps of: a) determining in saidsample an expression level of at least one miRNA selected from the groupconsisting of miRNAs having the sequence SEQ ID NO 59, SEQ ID NO 65, SEQID NO 1 to SEQ ID NO 58, SEQ ID NO 60 to SEQ ID NO 64 and SEQ ID NO 66to SEQ ID NO 170, b) comparing the pattern of expression level(s)determined in step a) with one or several reference pattern(s) ofexpression levels; and c) classifying the sample of said patient fromthe outcome of the comparison in step b) into one of at least twoclasses.
 2. A method for diagnosing Alzheimer's Disease, predicting riskof developing Alzheimer's Disease, or predicting an outcome ofAlzheimer's Disease in a patient suffering from or at risk of developingAlzheimer's Disease, said method comprising the steps of: a) determiningin a blood sample from said patient, the expression level of at leastone miRNA selected from the group consisting of miRNAs having thesequence SEQ ID NO 59, SEQ ID NO 65, SEQ ID NO 1 to SEQ ID NO 58, SEQ IDNO 60 to SEQ ID NO 64 and SEQ ID NO 66 to SEQ ID NO 170, b) comparingthe pattern of expression level(s) determined in step a) with one orseveral reference pattern(s) of expression levels; and c) diagnosingAlzheimer's Disease, predicting a risk of developing Alzheimer'sDisease, or predicting an outcome of Alzheimer's Disease from theoutcome of the com-parison in step b).
 3. The method according to claim1, wherein said at least one miRNA is selected from the group consistingof miRNAs having the sequence SEQ ID NO 59, SEQ ID NO 65, SEQ ID NO 1and SEQ ID NO
 56. 4. The method according to claim 1 comprising in stepa) determining the expression level of the miRNAs: brain-mir-112,hsa-miR-5010-3p, hsa-miR-103a-3p, hsa-miR-107, hsa-let-7d-3p,hsa-miR-532-5p, and brain-mir-161.
 5. The method according to claim 1,comprising in step a) determining the expression level of 5 miRNAsselected from the signatures consisting of brain-mir-112 hsa-miR-5010-3phsa-miR-1285-5p hsa-miR-151a-3p hsa-let-7f-5p, hsa-miR-3127-3phsa-miR-1285-5p hsa-miR-425-5p hsa-miR-148b-5p hsa-miR-144-5p,hsa-miR-3127-3p hsa-miR-3157-3p hsa-miR-148b-5p hsa-miR-151a-3phsa-miR-144-5p, hsa-miR-3127-3p hsa-miR-1285-5p hsa-miR-425-5phsa-miR-151a-3p hsa-miR-144-5p, hsa-miR-1285-5p brain-mir-112hsa-miR-5010-3p hsa-miR-151a-3p hsa-let-7a-5p, hsa-miR-5001-3phsa-miR-1285-5p hsa-miR-425-5p hsa-miR-148b-5p hsa-miR-144-5p,hsa-miR-3127-3p hsa-miR-1285-5p hsa-miR-148b-5p hsa-miR-151a-3phsa-miR-144-5p, hsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-425-5phsa-miR-148b-5p hsa-miR-144-5p, hsa-miR-1285-5p hsa-miR-5010-3phsa-miR-151a-3p hsa-miR-144-5p hsa-let-7a-5p, hsa-miR-1285-5pbrain-mir-112 hsa-miR-425-5p hsa-miR-151a-3p hsa-miR-144-5p,hsa-miR-5001-3p hsa-miR-1285-5p brain-mir-112 hsa-miR-151a-3phsa-let-7f-5p, hsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-148b-5phsa-miR-144-5p hsa-let-7f-5p, hsa-miR-1285-5p hsa-miR-3157-3phsa-miR-148b-5p hsa-miR-151a-3p hsa-miR-144-5p, hsa-miR-5001-3phsa-miR-1285-5p hsa-miR-5010-3p hsa-miR-151a-3p hsa-let-7f-5p,brain-mir-431 hsa-miR-1285-5p hsa-miR-3157-3p hsa-miR-151a-3phsa-miR-144-5p, hsa-miR-3127-3p hsa-miR-1285-5p brain-mir-112hsa-miR-425-5p hsa-miR-151a-3p, hsa-miR-1285-5p hsa-miR-5010-3phsa-miR-151a-3p hsa-miR-144-5p hsa-let-7f-5p, hsa-miR-550a-5phsa-miR-1285-5p brain-mir-112 hsa-miR-151a-3p hsa-let-7f-5p,hsa-miR-1285-5p brain-mir-112 hsa-miR-148b-5p hsa-miR-151a-3phsa-miR-144-5p, and hsa-miR-5001-3p brain-mir-112 hsa-miR-5010-3phsa-miR-151a-3p hsa-let-7f-5p.


6. The method according to claim 1, wherein the expression levels of aplurality of miRNAs are determined as expression level values and step(b) comprises mathematically combining the expression level values ofsaid plurality of miRNAs.
 7. The method according to claim 1, whereinthe determination of the expression level in step (a) is obtained by useof a method selected from the group consisting of a sequencing-basedmethod, an array-based method and a PCR-based method.
 8. The methodaccording to claim 1, wherein the determination of the expression levelsof at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 miRNAs are determinedto obtain a pattern of expression levels.
 9. The method according toclaim 1, comprising in step a) determining the expression level of themiRNAs:hsa-let-7f-5p, hsa-miR-1285-5p, hsa-miR-107, hsa-miR-103a-3p,hsa-miR-26b-3p, hsa-miR-26a-5p, hsa-miR-532-5p, hsa-miR-151a-3p,brain-mir-161, hsa-let-7d-3p, brain-mir-112, and hsa-miR-5010-3p.
 10. Akit for performing the method according to claim 1, said kit comprisingmeans for determining in said blood sample from said patient, anexpression level of at least one miRNA selected from the groupconsisting of miRNAs having the sequence SEQ ID NO 59, SEQ ID NO 65, SEQID NO 1 to SEQ ID NO 58, SEQ ID NO 60 to SEQ ID NO 64 and SEQ ID NO 66to SEQ ID NO
 170. 11. The kit of claim 10, further comprising at leastone reference pattern of expression levels for comparing with theexpression level of the at least one miRNA from said sample.
 12. Acomputer program product useful for performing the method according toclaim 1, comprising means for receiving data representing an expressionlevel of at least one miRNA in a patient blood sample selected from thegroup consisting of miRNAs with the sequence SEQ ID NO 59, SEQ ID NO 65,SEQ ID NO 1 to SEQ ID NO 58, SEQ ID NO 60 to SEQ ID NO 64 and SEQ ID NO66 to SEQ ID NO 170, means for receiving data representing at least onereference pattern of expression levels for comparing with the expressionlevel of the at least one miRNA from said sample, means for comparingsaid data representing the expression level of the at least one miRNA ina patient sample, and means for determining a diagnosis of Alzheimer'sDisease, a prediction of a risk of developing Alzheimer's Disease, or aprediction of an outcome of Alzheimer's Disease from the outcome of thecomparison in step b).